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Postdoctoral Researcher in visual cognitive computational neuroscience at the University of Cambridge (Kamila Maria Jozwik)
by Kamila Maria Jóźwik March 8, 2026

March 8, 2026

*Postdoctoral Researcher **in visual cognitive computational neuroscience* *Supervisor: *Dr. Kamila Maria Jozwik, Jozwik lab, University of Cambridge *Application deadline: *2 April 2026 *Application link: * https://www.cam.ac.uk/jobs/postdoctoral-research-associate-fixed-term-su490… *Start date*: October 2026 (there is some flexibility) The Jozwik lab studies visuo-semantic cognition combining cognitive science, neuroscience, and computational modelling. The lab’s research has focused on probing specific visual dimensions in the context of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_w…) We also began exploring how to apply our expertise in visuo-semantic cognition and AI to neurotechnology (Focused Ultrasound Stimulation) and understanding mental health conditions. The postdoctoral researcher is welcome to work on one (or more) of the three aspects of the research programme funded by the Royal Society that aims to disentangle and model behaviourally-relevant visual and semantic dimensions (characteristics of objects: ”curved”, ”pink”, ”having eyes”, “being animate”, ”having agency”, or ones that are hard to name) of visual cognition in the human brain, while increasing the ecological validity of experiments (including mobile EEG and immersive technologies), in the light of the below three aims. Note Dr. Jozwik would be happy to discuss projects related to these aims, as there is some flexibility in research directions. 1) characterise behaviourally-relevant visual and semantic dimensions by the use of large-scale brain imaging datasets of responses to images and model these representations with computational models and validate these predictions in follow-up neuroimaging experiments, 2) define and model dimensions related to the perception of animacy when interacting with objects and people using videos (behaviour, fMRI, and MEG), 3) determine to what extent these brain representations and dimensions change when humans are immersed in the environment (VR/AR and/or mobile EEG). *The ideal candidate* will have: - a PhD (or will have a PhD awarded by the time of the postdoctoral position start date) in a relevant field such as neuroscience, computational neuroscience, cognitive science, NeuroAI. or similar (essential) - extensive experience in programming in Python or Matlab and data analysis (essential) - experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis (essential) - track record of first-author publications (essential) - experience in machine learning and AI - a collaborative approach to doing science and willingness to help other lab members - curiosity and motivation to work on the proposed or related research questions. *Before applying, please contact Kamila Maria Jozwik* (Royal Society University Research Fellow and an Assistant Research Professor, jozwik.kamila(a)gmail.com or kj287(a)cam.ac.uk) In the initial email, please include: - your CV - information about the programming, computational modelling, and relevant research, data collection and analysis experience (fMRI, M/EEG, neuromodulation, electrophysiology, behaviour) - details of journal and conference publications, preprints, and research theses - Please also ask 2-3 of your referees, ideally with whom you have worked on research projects, to email their reference letters to Dr. Jozwik. (If you have any queries regarding the application process, please contact hr(a)mrc-cbu.cam.ac.uk.) *Lab research environment*: The Jozwik lab is based at the MRC Cognition and Brain Sciences Unit, University of Cambridge, with links to broader Cambridge (e.g., Cambridge NeuroWorks powered by Advanced Research and Invention Agency) and international scientific ecosystems (e.g., the Center for Brains, Minds & Machines, now MIT Quest for Intelligence). The Unit has an on-site 3T fMRI scanner (with access to a 7T fMRI scanner within cycling distance), an MEG scanner, EEG systems, Focused Ultrasound, Transcranial Magnetic Stimulation, and dedicated methods and computing support staff. The Unit runs two MPhil Programs: Cognitive Neuroscience and NeuroAI, and postdoctoral researchers have the opportunity to supervise MPhil students from these and other MPhil programmes and to contribute to supervising PhD students. Postdoctoral researchers are supported in applying for postdoctoral fellowships. The lab values commitment to rigorous, open science, supports diversity in all its meanings, and drives curiosity in a supportive, multidisciplinary, and international research environment.

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PhD position in visual cognitive computational neuroscience at the University of Cambridge (Kamila Maria Jozwik)
by Kamila Maria Jóźwik March 8, 2026

March 8, 2026

*PhD in visual cognitive computational neuroscience* *Supervisor: *Dr. Kamila Maria Jozwik, Jozwik lab, University of Cambridge *Application deadline: *13th March 2026 *Application link: * https://www.postgraduate.study.cam.ac.uk/courses/directory/cvbspdbsc *PhD fees status: *Home fees only ( https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status) 4 years, fully funded *Start date*: October 2026 The Jozwik lab studies visuo-semantic cognition combining cognitive science, neuroscience, and computational modelling. The lab’s research has focused on probing specific visual dimensions in the context of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_w… ). We also began exploring how to apply our expertise in visuo-semantic cognition and AI to neurotechnology (Focused Ultrasound Stimulation) and understanding mental health conditions. The PhD student is welcome to work on one (or more) of the three aspects of the research programme funded by the Royal Society that aims to disentangle and model behaviourally-relevant visual and semantic dimensions (characteristics of objects: ”curved”, ”pink”, ”having eyes”, “being animate”, ”having agency”, or ones that are hard to name) of visual cognition in the human brain, while increasing the ecological validity of experiments (including mobile EEG and immersive technologies), in the light of the below three aims. Note Dr. Jozwik would be happy to discuss PhD projects related to these aims, as there is some flexibility in research directions. 1) characterise behaviourally-relevant visual and semantic dimensions by the use of large-scale brain imaging datasets of responses to images and model these representations with computational models and validate these predictions in follow-up neuroimaging experiments, 2) define and model dimensions related to the perception of animacy when interacting with objects and people using videos (behaviour, fMRI, and MEG), 3) determine to what extent these brain representations and dimensions change when humans are immersed in the environment (VR/AR and/or mobile EEG). *The ideal candidate* will have: - extensive experience in programming in Python or Matlab and data analysis (essential, please note that coursework coding during an undergraduate or Master’s degree will likely not be enough) - substantial research experience (essential, e.g., through research MPhil/Master’s degree, or research assistant job) - experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis - experience in machine learning and AI - a collaborative approach to doing science and willingness to help other lab members - curiosity and motivation to work on the proposed or related research questions. *Before applying, please contact Kamila Maria Jozwik* (Royal Society University Research Fellow and Assistant Research Professor, jozwik.kamila(a)gmail.com or kj287(a)cam.ac.uk) In the initial email, please include: - your CV - information about your programming, computational modelling, and relevant research, data collection and analysis experience (fMRI, M/EEG, neuromodulation, electrophysiology, behaviour) - details of journal and conference publications, preprints, and research theses - Please also ask 2-3 of your referees, ideally with whom you have worked on research projects, to email their reference letters to Dr. Jozwik. *Lab research environment*: The Jozwik lab is based at the MRC Cognition and Brain Sciences Unit, University of Cambridge, with links to broader Cambridge (e.g., Cambridge NeuroWorks powered by Advanced Research and Invention Agency) and international scientific ecosystems (e.g., the Center for Brains, Minds & Machines, now MIT Quest for Intelligence). The Unit has an on-site 3T fMRI scanner (with access to a 7T fMRI scanner within cycling distance), an MEG scanner, EEG systems, Focused Ultrasound, Transcranial Magnetic Stimulation, and dedicated methods and computing support staff. The Unit runs two MPhil Programs: Cognitive Neuroscience and NeuroAI, and PhD students have the opportunity to supervise MPhil students. The lab values commitment to rigorous, open science, supports diversity in all its meanings, and drives curiosity in a supportive, multidisciplinary, and international research environment.

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Opportunity for postdocs and senior grad students
by Goodhill, Geoffrey March 8, 2026

March 8, 2026

The Center for Theoretical and Computational Neuroscience (ctcn.wustl.edu) at Washington University in St. Louis invites applications from postdocs and senior graduate students, who are using primarily theoretical and computational techniques to study the principles of brain organization and function, for its third Computational Neuroscience Next Generation symposium, September 14-15th 2026: https://ctcn.wustl.edu/2026-next-gen-symposium The CTCN will pay travel expenses for successful applicants. Participants will engage with researchers at CTCN-participating departments over two days, including: - Presenting their work during the symposium, which will also feature leading WashU neuroscientists. - One-on-one meetings with faculty. - Informal discussions with the Washington University computational and theoretical neuroscience community. Being selected as a Next Generation Symposium speaker is an exciting opportunity s to share your research with Washington University computational neuroscientists, to receive feedback, to explore postdoctoral opportunities including CTCN postdoctoral fellowships (https://ctcn.wustl.edu/postdoc-fellows) and to form professional and intellectual relationships with peers, mentors and potential future collaborators. The application form is available at https://ctcn.wustl.edu/2026-next-gen-symposium. The application deadline is April 24th. Professor Geoffrey J Goodhill Departments of Developmental Biology and Neuroscience Director, Center for Theoretical and Computational Neuroscience (ctcn.wustl.edu) Affiliate appointments: Physics, Biomedical Engineering, Computer Science and Engineering, and Electrical and Systems Engineering Washington University School of Medicine 4370 Duncan Ave. St Louis, MO 63110 g.goodhill(a)wustl.edu https://neuroscience.wustl.edu/people/geoffrey-goodhill-phd

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DeepLearn 2026: early registration March 29
by David Silva - IRDTA March 7, 2026

March 7, 2026

****************************************************** 13th INTERNATIONAL SCHOOL ON DEEP LEARNING DeepLearn 2026 Orléans, France July 20-24, 2026 https://deeplearn.irdta.eu/2026/ ****************************************************** Co-organized by: University of Orléans Centre Val de Loire Doctoral College Institute for Research Development, Training and Advice – IRDTA Luxembourg/London ****************************************************** Early registration: March 29, 2026 ****************************************************** SCOPE: DeepLearn 2026 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Luleå, Bournemouth, Bari, and Porto. Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedicine and healthcare, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, business and finance, biotechnology, physics and astrophysics, biometrics, communications, climate sciences, geographic information systems, signal processing, genomics, materials design, video technology, social systems, earth and sustainability, mathematical proofs, etc. etc. The field is also raising a number of relevant questions about efficiency and robustness of the algorithms, explainability, transparency, interpretability, risks and safety, as well as important ethical concerns at the frontier of current knowledge that deserve careful multidisciplinary discussion. Most deep learning subareas will be displayed and main challenges identified through 16 four-hour and a half courses, 2 keynote lectures, 1 round table, and a hackathon competition among participants. Renowned academics and industry pioneers will lecture and share their views with the audience. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely. ADDRESSED TO: Graduates, postgraduates and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, hence people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2026 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators. VENUE: DeepLearn 2026 will take place in Orléans, located in the heart of the Loire Valley, which was declared by UNESCO a World Heritage Site in 2000. The venue will be: University of Orléans Faculty of Law, Economics and Management 11 rue de Blois 45100 Orléans, France https://www.univ-orleans.fr/en STRUCTURE: 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another. All lectures will be videorecorded. Participants will be able to watch them again for 45 days after the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes. Also companies will be able to present their industrial developments for 10 minutes. The school will include a hackathon, where participants will be able to work in teams to tackle several machine learning challenges. Full live online participation will be possible. The organizers highlight, however, the importance of face to face interaction and networking in this kind of research training event. KEYNOTE SPEAKERS: Yingbin Liang (Ohio State University), Convergence Theory: How Fast Do Discrete Diffusion Models Generate? Le Song (Mohamed bin Zayed University of Artificial Intelligence), Towards AI-Driven Digital Organism: A System of Multiscale Foundation Models for Biology PROFESSORS AND COURSES: Nitesh Chawla (University of Notre Dame), [intermediate] Learning from Imbalanced Data Yuejie Chi (Yale University), [introductory/intermediate] Statistical and Algorithmic Foundations of Reinforcement Learning Bo Han (Hong Kong Baptist University), [introductory/intermediate] Trustworthy Machine Learning from Data to Models Jiawei Han (University of Illinois Urbana-Champaign), [intermediate] Structure-Guided, Theme-Based Knowledge Discovery with Large Language Models Mingyi Hong (University of Minnesota), [intermediate] Modern Optimization Algorithms for Large Language Models Cho-Jui Hsieh (University of California Los Angeles), [intermediate/advanced] Optimizers for Large Language Model Training Furong Huang (University of Maryland), [advanced] Generative AI Agents Tara Javidi (University of California San Diego), [intermediate] Active Physical Intelligence for Industrial Scale Monitoring Yan Liu (University of Southern California), [intermediate] Time Series Foundation Models: From Forecasting to Reasoning Zhijin Qin (Tsinghua University), [intermediate/advanced] Semantic Communications Aarti Singh (Carnegie Mellon University), [intermediate] Human Centered AI: Challenges and Opportunities Suvrit Sra (Technical University of Munich), [introductory/intermediate] Introduction to the Theory of Learning with Transformers Ivor Tsang (A*STAR Centre for Frontier AI Research), [introductory/intermediate] Long-Horizon Agentic Intelligence Ming-Hsuan Yang (University of California Merced), [advanced] Recent Advances in Multimodal Understanding and Generation Tong Zhang (University of Illinois Urbana-Champaign), [introductory/intermediate] Reinforcement Learning for Large Language Models Jun Zhu (Tsinghua University), [introductory/advanced] Generative Models: from Virtual to Physical World OPEN SESSION: An open session will collect 5-minute voluntary oral presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david(a)irdta.eu by July 12, 2026. INDUSTRIAL SESSION: A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Abstracts have to be submitted to david(a)irdta.eu by July 12, 2026. HACKATHON: A hackathon will take place, where participants can voluntarily work in teams to tackle several machine learning challenges. They will be coordinated by Professor Sergei V. Gleyzer (University of Alabama). The challenges will be released 2 weeks before the beginning of the school. A jury will judge the submissions and the winners of each challenge will be announced by the end of August 2026. The winning teams will receive a modest monetary prize and the runners-up will get a certificate. SPONSORS: Companies/institutions/organizations willing to be sponsors of the event can download the sponsorship leaflet from https://deeplearn.irdta.eu/2026/sponsors/ ORGANIZING COMMITTEE: Karim Abed-Meraim (Orléans, local co-chair) Sergei V. Gleyzer (Tuscaloosa, hackathon chair) Meryem Jabloun (Orléans, local co-chair) Carlos Martín-Vide (Tarragona, program chair) Santiago Montes (Tarragona, webpage) Sara Morales (Luxembourg, finances) Florian Nowicki (Orléans, social networks) Philippe Ravier (Orléans, local chair) David Silva (London, organization chair) REGISTRATION: It has to be done at https://deeplearn.irdta.eu/2026/registration/ The selection of 6 courses requested in the registration template is only tentative and non-binding. For logistical reasons, it will be helpful to have an estimation of the respective demand for each course. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event. FEES: Fees comprise access to all program activities and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same. ACCOMMODATION: Accommodation suggestions are available at https://deeplearn.irdta.eu/2026/accommodation/ CERTIFICATE: A certificate of successful participation will be delivered indicating the number of hours of academic activities (40). This should be sufficient for those participants who plan to request ECTS recognition from their home university. QUESTIONS AND FURTHER INFORMATION: david(a)irdta.eu ACKNOWLEDGMENTS: Université d’Orléans Collège Doctoral Centre-Val de Loire Universitat Rovira i Virgili Institute for Research Development, Training and Advice – IRDTA, Luxembourg/London

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🧠[Meetings] [CFP] IEEE SMC 2026 Workshop - NeuroWearX for Empowered Co-Intelligence: Advancing Human–Machine Interfaces by Integrating Biological, Physical, and Spatial Intelligence with Generative AI
by NeuroWearX March 6, 2026

March 6, 2026

Call for Papers 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026) https://www.ieeesmc2026.org/ IEEE SMC Workshop: NeuroWearX for Empowered Co-Intelligence: Advancing Human–Machine Interfaces by Integrating Biological, Physical, and Spatial Intelligence with Generative AI Overview Wearables, assistive robots, and smart IoT systems are rapidly becoming part of everyday life. However, many of today’s wearable computing and human–machine interfaces still feel “smart, yet not quite helpful.” They are often fragmented, difficult to personalize, and frequently struggle to transform rich but noisy multimodal signals, such as physiology (e.g., heart rate, neural/muscle activity), body movement, and environmental context, into reliable, meaningful real-world support. At the same time, generative AI and foundation/world models are reshaping the landscape, shifting the paradigm beyond isolated sensors and single-purpose algorithms toward integrated, adaptive, context-aware Human–AI–Machine systems. To make the use of these technologies feel like a natural extension of ourselves, this workshop brings together researchers and practitioners across biosensing and measurement, neuroscience, AI, robotics, ubiquitous computing, and human-centered design to define the next frontier of Empowered Co-Intelligence: wearable interfaces that fuse three complementary “intelligences”—(1) biological intelligence, to infer human state and intent by modeling how people naturally think and behave; (2) physical/embodied intelligence, which understands body dynamics and real-world physics so interaction and assistance remain safe and effective; and (3) spatial intelligence, to leverage nearby sensors, IoT devices and smart environments for continuous situational awareness. These capabilities are further amplified by generative AI—models trained on large-scale data that can integrate heterogeneous signals to predict, reason, and adapt—enabling systems to become more personalized and context-aware. Together, these capabilities can overcome the limits of noisy on-body sensing and constrained wearable computing by turning fragmented measurements into coherent, actionable assistance. Our vision is an AI that operates quietly in the background, like an invisible layer of artificial cortex, running in parallel with our own cerebral cortex and coordinating across different lobes: not simply following rules, but learning your patterns, anticipating your needs, and adjusting in real time. The result is 24/7 support for thinking, decision-making, and physical action that feels intuitive, seamless, and requires minimal cognitive effort. Beyond new algorithms, we also emphasize human-centered evaluation, trust, accessibility, and inclusive augmentation, with the goal of accelerating research that advances wearable computing into reliable, scalable, and equitable systems—technologies that truly co-evolve with users over time. Topics of Interest We welcome research papers, short/WiP papers, and position/vision papers on (but not limited to) the following areas aligned with multimodal perception–decision–action cycles, shared autonomy, personalization, safety, and real-world robustness on wearable technologies. (A) Biological intelligence: sensing human state & intent ● Multimodal biosensing: EEG/EMG/ECG/EDA/PPG/respiration, inertial + physiological fusion ● Robust biosignal decoding in-the-wild: drift handling, motion artifacts, missing data, calibration-free methods ● Intent recognition and user-state estimation (fatigue, stress, attention, readiness, motor intent) ● Personalized adaptation across users: domain adaptation, continual learning, few-shot personalization ● Privacy-preserving on-body learning and secure biosignal pipelines (B) Physical intelligence: embodied assistance & safe action ● Embodied AI for wearable augmentation: biomechanics, dynamics, control, and safe shared autonomy ● Wearable robotics and human–robot physical interaction (exoskeletons, prostheses, assistive devices) ● Safety, stability, and fail-safe design in closed-loop wearable control ● Human factors and ergonomics for physical assistance; workload-aware support ● Verification/validation of embodied policies for assistive wearable systems (C) Spatial intelligence: context from environments, robots & smart IoT ● Context-aware wearable computing using smart environments, robots, and IoT integration ● Scene understanding for assistance: activity context, objects, layout, hazards, and social context ● Multi-device sensing orchestration (wearable + phone + AR + ambient sensors) ● Real-world robustness and interoperability across heterogeneous devices (D) Generative AI & foundation/world models for wearables ● Generative AI / foundation models for wearable time-series, biosignal representation learning, multimodal fusion ● World models for prediction, planning, and co-adaptation in human-centered wearable interaction ● Edge/on-body inference: efficiency, compression, distillation, and low-power deployment ● Uncertainty-aware assistance, reliable decision-making, and “AI-in-the-background” support (E) Human-centered NeuroDesign, evaluation & impact ● Human-centered interface design: intuitive/subconscious interaction, trust, transparency, explainability ● UX evaluation in real-world settings: accessibility, equity, inclusive augmentation ● Ethical, privacy, and governance considerations for always-on wearable intelligence ● Application domains: assistive augmentation, rehabilitation, health monitoring, everyday support Submission Instructions ● Submission deadline: March 22, 2026 ● Submission site at 2026 IEEE International Conference on Systems, Man, and Cybernetics (Papercept): https://conf.papercept.net/conferences/scripts/start.pl ● Submission code: dt3i1 (Use the submission code "dt3i1" during the Papercept submission process to route your paper to this workshop.) ● Please follow the IEEE SMC 2026 submission guidelines and formatting requirements. IEEE SMC 2026 submission guidelines: https://www.ieeesmc2026.org/call-for-papers Best regards, Organizing Committee 🧠 NeuroWearX Workshop @ IEEE SMC 2026 [image: FullLogo_Transparent_NoBuffer.png] Website: https://www.ieeesmc2026.org/Content/3015153.html Email: info.neurowearx(a)gmail.com

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WashU CTCN Postdoc Fellows
by Goodhill, Geoffrey March 5, 2026

March 5, 2026

POSTDOCTORAL FELLOWSHIPS AVAILABLE AT WASHINGTON UNIVERSITY IN ST LOUIS The Center for Theoretical and Computational Neuroscience (ctcn.wustl.edu) at Washington University in St Louis invites applications from outstanding Postdoctoral Fellows to work at the interface between theoretical and experimental neuroscience labs at WashU. Deadline for applications is May 1st 2026. The CTCN is a joint initiative between the Schools of Medicine, Engineering, and Arts and Sciences at WashU, and provides a hub for neuroscientists to collaborate with mathematicians, physicists and engineers to find creative solutions to some of the most difficult problems currently facing neuroscience and artificial intelligence. Each CTCN Postdoctoral Fellow is based in at least two labs, but also has the opportunity to seek out new collaborations which help build new connections within the WashU community. We are looking for people with drive, independence and outstanding prior achievement, who are committed to leveraging interdisciplinary collaboration to drive forward the field of theoretical and computational neuroscience. Washington University in St Louis is ranked in the top 10 worldwide for Neuroscience and Behavior. Salary for CTCN Fellows is significantly above standard NIH postdoc rates, and funds for conference travel are included. In addition, WashU offers excellent benefits and comprehensive access to career development, professional and personal support. The St Louis metropolitan area has a population of almost 3M and is rich in culture, green spaces and thriving music and arts scenes, with a highly accessible cost of living. For more details on this prestigious Fellowship opportunity, including how to apply, please see https://ctcn.wustl.edu/postdoc-fellows Professor Geoffrey J Goodhill Departments of Developmental Biology and Neuroscience Affiliate appointments: Physics, Biomedical Engineering, Computer Science and Engineering, and Electrical and Systems Engineering Washington University School of Medicine 660 S. Euclid Avenue St. Louis, MO 63110 g.goodhill(a)wustl.edu https://neuroscience.wustl.edu/people/geoffrey-goodhill-phd

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NEST Conference 2026 Abstract Submission and Registration open
by Hans Ekkehard Plesser March 5, 2026

March 5, 2026

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2026. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. Take the opportunity to advance your skills in using NEST at our hands-on workshops! We explicitly encourage young scientists to participate in the conference! This year's conference will again take place as a virtual conference on Tuesday/Wednesday 16/17 June 2026. We are delighted to welcome * Marja-Leena Linne, Tampere University * Maxime Carriere and Fynn Dobler, Freie Universität Berlin * Agnes Korcsak-Gorzo, Forschungszentrum Jülich * Pablo Martínez Cañada, University of Granada as one of our keynote speakers at this year’s conference. Registration and submission of contributions are now open! We are inviting you to submit contributions in the form of talks and "posters". Please register and submit your contribution(s) via the conference website https://nest-simulator.org/conference. Important dates: 10 April 2026 - Deadline for submission of contributions 05 May 2026 - Notification of acceptance 09 June 2026 - Registration deadline We are looking forward to seeing you all in June! Hans Ekkehard Plesser and the conference organizing committee -- Prof. Dr. Hans Ekkehard Plesser Research Committee Chair, Faculty of Science and Technology Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser

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Study Computational & AI-Centered Cognitive Science in Vienna
by Moritz Grosse-Wentrup March 5, 2026

March 5, 2026

Do you want to revolutionize how we understand the mind and brain using AI, machine learning, and data-driven methods? The **Middle European interdisciplinary master’s programme in Cognitive Science (MEi:CogSci@Univie)** is a two-year, English-taught master program at the University of Vienna that combines - **strong training in quantitative methods** (statistics, programming, signal processing, machine learning, AI) and - **flexible, student-driven specialization in one or more core domains of cognitive science** - you choose your focus area(s) from among neuroscience, biology, linguistics, anthropology, philosophy, and psychology, and can tailor your curriculum accordingly, - with **early involvement in research** in small, interdisciplinary, and international teams. We are looking for applicants who either - hold a bachelor degree in one of the core disciplines of cognitive science (biology, computer science, linguistics, philosophy, psychology, social and cultural anthropology, cognitive science) and wish to develop a strong computational and AI-focused profile, or - hold a bachelor degree in a field with a strong background in quantitative methods (e.g. mathematics, physics, engineering, or other natural sciences) and wish to build on this foundation to master AI techniques and apply their knowledge to a domain of cognitive science of their choice. **Key facts for the new cohort (winter term 2026/27):** - **Application period:** Open now; closes on 7 April - **Details and online application:** https://ssc-phil.univie.ac.at/studien/middle-european-interdisciplinary-mas… - **Start of semester:** 1 October 2026 If you are excited about using AI to push the boundaries of cognitive science - and want the freedom to shape your own specialization - we encourage you to apply for the MEi:CogSci@Univie cohort starting in the winter term 2026/27. Best regards, Moritz Grosse-Wentrup Study Director MEi:CogSci@Univie University of Vienna

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Neural Data Analysis Workshop before FENS forum (Jun 3-4, 2026)
by Edoardo Balzani March 4, 2026

March 4, 2026

Excited to share a great opportunity for systems neuroscientists! This July, right before the FENS forum, the Flatiron Institute Center for Computational Neuroscience of the Simons Foundation is hosting a hands-on workshop <https://www.simonsfoundation.org/event/ccn_fens2026/?swcfpc=1> on neural data analysis in Barcelona, Spain. *Workshop Details:* *What: *2-day workshop on pynapple <https://pynapple.org/> & NeMoS <https://nemos.readthedocs.io/en/latest/#> *When: *Jul 3–4, 2026 *Where: *Melia Barcelona Sky Hotel Pere IV, 272 - 286 Spain *Who:* Grad students & postdocs analyzing electrophysiology or calcium imaging data*. *Applicants located in Europe and Africa will be prioritized. Via live-coding and hands-on group projects, you will learn to: - Use pynapple for neural data manipulation and exploration - Build statistical models with NeMoS (powered by JAX with GPU acceleration) *Cost:* Accommodation & meals provided, no participation fees. Both packages are open-source Python tools developed at Flatiron CCN <https://www.simonsfoundation.org/flatiron/center-for-computational-neurosci…> to streamline neural data analysis and modeling. *Apply here:* https://simonsfoundation.formstack.com/forms/neural_data_analysis_workshop_… *Learn more:* https://www.simonsfoundation.org/event/ccn_fens2026/?swcfpc=1 Please feel free to share with anyone who might be interested! Best regards, Edoardo Balzani -- *Edoardo Balzani* Associate Research Scientist Center for Computational Neuroscience Flatiron Institute

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Call for Proposals for Satellite Workshops at the Bernstein Conference 2026 is open now
by Bernstein Conference March 4, 2026

March 4, 2026

+++ The Bernstein Network Computational Neuroscience invites proposals for Satellite Workshops at the Bernstein Conference 2026 in Frankfurt am Main, Germany. +++ ____ *Bernstein Conference* Each year, the Bernstein Network invites the international computational neuroscience community to the Bernstein Conference for intensive scientific exchange. It has established itself as one of the most renowned conferences worldwide in this field, attracting students, postdocs, and PIs from around the world to meet and discuss new scientific discoveries.** https://www.bernstein-conference.de/ <https://www.bernstein-conference.de/> ____ ** *Satellite Workshops* Satellite Workshops at the Bernstein Conference 2026 provide a forum to discuss topical research questions, novel scientific approaches, and challenges in computational neuroscience and related fields. Ideally, the format should foster extensive scientific discussions and debates, and go beyond a mere series of talks. https://bernstein-network.de/bernstein-conference/call-for-satellite-worksh… <https://bernstein-network.de/bernstein-conference/call-for-satellite-worksh…> ____ ** *Important Dates* Satellite Workshops: *September 28 - 29, 2026* ·Monday, Sep 28, 14:00 - 18:30 CEST ·Tuesday, Sep 29, 8:30 - 12:30 CEST Main Conference: September 29 - October 1, 2026 Deadline for Satellite Workshop proposal submission: *April 29, 2026 at 15:00 CEST* Notification of Satellite Workshop acceptance: May 2026 ____ ** *Benefits* For each workshop, the organizers will receive *one fee waiver* for the Satellite Workshops, which they can award to one of the workshop speakers. We strongly recommend that the waiver is issued giving consideration to aspects of inclusion. ** ____ *INVITED SPEAKERS* Roshan Cools (Donders Institute for Brain, Cognition and Behaviour, The Netherlands) Benjamin Grewe (Institute of Neuroinformatics of the ETH and UZH, Switzerland) Andreas Herz (LMU Munich, Germany) Ewelina Knapska (Nencki Institute of Experimental Biology, Poland) John Krakauer (Champalimaud Foundation, Portugal) Jeehyun Kwag (Seoul National University, Korea) Anna Levina (University of Tübingen, Germany) Bence Ölveczky (Harvard University, USA) Sandro Romani (Janelia Research Campus, USA) ____ *CONFERENCE COMMITTEE* Stefan Rotter (Conference Chair) Matthias Kaschube (Conference Host) Tim Vogels (Program Chair) Panayiota Poirazi (Program Vice Chair) Katharina Wilmes (Workshop Chair) Juan Álvaro Gallego(Workshop Vice Chair) & Nicolas Brunel, Alex Cayco-Gajic, Monika Jadi, Jennifer Li, Scott Linderman, Wiktor Młynarski, Richard Naud, Joseph Raimondo, Tatyana Sharpee, Taro Toyoizumi, Eleni Vassilaki ____ For any further questions, please contact: bernstein.conference(a)fz-juelich.de <mailto:bernstein.conference@fz-juelich.de>

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[COMPLEX NETWORKS 2026] CFP — Submission Now Open (Granada, Dec 2–4, 2026)
by Hocine Cherifi March 4, 2026

March 4, 2026

*Complex Networks 2026* 15th International Conference on Complex Networks and Their Applications 📍 Granada 🗓 December 02–04, 2026 (Tutorials: December 01) 📝 Submission deadline: September 02, 2026 The website and submission system are now open. We invite original research contributions on theoretical foundations, methodological advances, and interdisciplinary applications of complex networks across data science, physics, computer science, mathematics, and the social sciences. Accepted papers will be published in the conference proceedings. Selected high-quality contributions will be invited to submit extended versions to special issues in journals such as: o Applied Network Science <https://appliednetsci.springeropen.com/> edited by Springer o Advances in Complex Systems <https://www.worldscientific.com/toc/acs/24/02> edited by World Scientific o Complex Systems <https://www.complex-systems.com/about/> o Entropy <https://www.mdpi.com/journal/entropy> edited by MDPI o PLOS COMPLEX SYSTEMS <https://journals.plos.org/complexsystems/> o PLOS <https://journals.plos.org/plosone/> one <https://journals.plos.org/plosone/> o Social Network Analysis and Mining <https://link.springer.com/journal/13278> edited by Springer Conference website: https://complexnetworks.org/ Submission portal: [https://cmt3.research.microsoft.com/COMPLEXNETWORKS2026/] <https://cmt3.research.microsoft.com/COMPLEXNETWORKS2026/> We look forward to welcoming the community to Granada in December 2026. The Organizing Committee Join us at COMPLEX NETWORKS 2026 <https://www.complexnetworks.org/> *-------------------------* Hocine CHERIFI Laboratoire* I*nterdisciplinaire *C*arnot de *B*ourgogne - ICB UMR 6303 CNRS Université Bourgogne Europe Editor in Chief Plos Complex Systems <https://plos.org/complex-systems-research-journal/#:~:text=PLOS%20Complex%2….> Founding & Adisory Editor Applied Network Science <https://appliednetsci.springeropen.com/> Editorial Board member IEEE ACCESS <https://ieeeaccess.ieee.org/?http%3A%2F%2Fieeeaccess_ieee_org%2F>, Scientific Reports <https://www.nature.com/srep/>, Journal of Imaging <https://www.mdpi.com/journal/jimaging>, Quality and Quantity <https://www.springer.com/journal/11135/>, Computational Social Networks <https://computationalsocialnetworks.springeropen.com/>, Complex Systems <https://www.complex-systems.com/> Evolutionary Intelligence <https://link.springer.com/journal/12065>

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Postdoctoral Position in Electrophysiology and Computational Modeling of Language
by Kuperberg, Gina R. March 3, 2026

March 3, 2026

Postdoctoral Position in Electrophysiology and Computational Modeling of Language (Start date: by June 1, 2026) The NeuroCognition of Language Lab (PI: Gina Kuperberg), jointly based at Tufts University and the Martinos Center for Biomedical Imaging at Massachusetts General Hospital (Boston), invites applications for a fully funded, two-year postdoctoral position. Applications will be reviewed on a rolling basis until the position is filled, with an intended start date of June 1, 2026 This position offers opportunities to pursue theory-driven research at the intersection of psycholinguistics, computational neuroscience, and electrophysiology. We are especially interested in applicants whose expertise aligns with one or more of the following areas: 1. Electrophysiology and predictive processing in sentence and discourse comprehension Using MEG and/or EEG to characterize the spatiotemporal dynamics of predictive processing during real-time language comprehension, with an emphasis on sentence- and discourse-level mechanisms. 2. Computational modeling of comprehension Developing mechanistic models (e.g., predictive coding simulations), and linking model behavior to behavioral and neural signatures of comprehension. We are also interested in decision-making and confidence-updating frameworks that formalize how comprehenders evaluate uncertainty, accumulate error evidence, and engage reprocessing following linguistic errors. 3. Language production in schizophrenia and computational language analysis Analyzing large-scale corpora of patient speech, using computational language methods, including Large Language Models (LLMs), to quantify and understand disorganized language production in schizophrenia. Applicants must hold a Ph.D. in cognitive science, psycholinguistics, psychology, linguistics, neuroscience, computational neuroscience, or a related field. Candidates may be strongest in one area (electrophysiology, computational modeling, or NLP/LLM-based language analysis), while demonstrating interest in the broader research program. Core qualifications * Strong interest in mechanistic accounts of sentence and discourse comprehension and its neural implementation * Strong computational and programming skills (Python, MATLAB, and/or R) * Experience with linear mixed-effects models and related statistical approaches Additional qualifications (one or more, depending on area of focus) * Experience collecting and/or analyzing EEG and/or MEG data * Experience developing and testing computational models of comprehension, for example predictive coding simulations * Experience applying computational language analysis methods, including distributional semantic similarity and embedding-based metrics, topic models, and LLM-based approaches, to understanding behavioral and neural data from naturalistic language paradigms For more about our lab, see our website: https://kuperberglab.com/ Massachusetts General Hospital and Tufts University are equal opportunity and affirmative action employers. Full-time employees receive full benefits. To apply, please send the following materials to Gina Kuperberg, M.D., Ph.D. (gkuperberg(a)mgh.harvard.edu<mailto:gkuperberg@mgh.harvard.edu>) and cc Arim Choi (arim.choi(a)tufts.edu<mailto:arim.choi@tufts.edu>): 1. Cover letter describing research experience and interests, including (i) which project area(s) you are applying to, and (ii) a brief statement of fit connecting your interests to the lab’s approach (for example, by referencing 1–2 relevant papers from our group or closely related work) 2. Curriculum Vitae 3. Names and contact details of references 4. PDFs of published or submitted papers ---------------------------------- Gina Kuperberg, MD PhD Dennett Stibel Professor in Cognitive Science, Department of Psychology, Tufts University Psychiatrist, Department of Psychiatry, Mass. General Hospital Tel: 617 863-0727 Fax: 617 812-4799 email: Gina.Kuperberg(a)tufts.edu OR GKuperberg(a)mgh.harvard.edu website: https://kuperberglab.com/

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Online International Symposium on Unified Theory of Prediction and Action, March 13–14
by Takuya Isomura March 3, 2026

March 3, 2026

Dear Colleagues, We are delighted to announce that the International Symposium on Unified Theory of Prediction and Action will be held online on March 13–14. https://unifiedtheory.jp/symposium/2026/index.html Invited Speakers: Andre Bastos (Vanderbilt University) Zenas Chao (The University of Tokyo) Anne Churchland (University of California, Los Angeles) Hiroshi Makino (Keio University) Jun Tani (Okinawa Institute of Science and Technology) Pablo Lanillos (Spanish National Research Council) Philip Corlett (Yale University) Xiaosi Gu (Yale University) The symposium will feature the integration of theory and experiments in neuroscience. We sincerely look forward to your participation. ---------- The 1st International Symposium Unified Theory of Prediction and Action Date: March 13 (Friday) and 14 (Saturday), 2026 (Starting around 9:30 on 13th and ending around 17:00 on 14th) Venue: Hybrid (Now accepting online registration only) Registration Form: https://forms.office.com/r/XVUh9qvAHQ Schedule: (in Japan Standard Time; JST) 13 March 2026 9:15 Registration 9:45 Opening Session 1: Predictive Coding 9:50 Andre Bastos, Vanderbilt University 10:40 Takuya Isomura, RIKEN 11:10 Break 11:30 Zenas Chao, The University of Tokyo 12:20 Misako Komatsu, Institute of Science Tokyo 12:50 Lunch Session 2: Control and Planning 14:00 Anne K. Churchland, UCLA 14:50 Teppei Ebina, The University of Tokyo 15:20 Break 15:40 Hiroshi Makino, Keio University 16:30 Hitoshi Okamoto, RIKEN 17:00 Poster session 18:00- Social event - Information exchange discussion 14 March 2026 Session 3: Session 3: NeuroAI 9:30 Jun Tani, Okinawa Institute of Science and Technology 10:20 Masahiro Suzuki, The University of Tokyo 10:50 Break 11:10 Pablo Lanillos, Spanish National Research Council 12:00 Akihiro Funamizu, The University of Tokyo 12:30 Lunch Session 4: Computational Psychiatry 13:30 Philip R. Corlett, Yale University 14:20 Hidehiko Takahashi, Institute of Science Tokyo 14:50 Break 15:10 Xiaosi Gu, Yale University 16:00 Kenji Doya, Okinawa Institute of Science and Technology 16:30 Closing Organizer: JSPS Transformative Research Area (A): Unified Theory of Prediction and Action Contact: https://unifiedtheory.jp/contact/ Please write “Regarding the International Symposium” in the subject line of the web inquiry form. ---------- Best regards, Takuya Isomura Brain Intelligence Theory Unit, RIKEN Center for Brain Science

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Independent PI Position – NeuroAI, Generative Models & Visual Computation (University of Coimbra)
by Jorge Almeida March 2, 2026

March 2, 2026

Dear all, Apologies for the cross-posting. We are seeking a researcher interested in establishing themselves as an independent PI under a 2–3 year contract in Portugal, to develop and lead a research line in NeuroAI and Visual Computation at the University of Coimbra. The successful candidate will build an independent program at the intersection of computational neuroscience and artificial intelligence, with a particular focus on visual processing. We are particularly interested in candidates working at the intersection of artificial intelligence and biological vision, including research on diffusion models and generative AI; GANs and large-scale visual generative systems; representation learning and mid-level visual features; image synthesis, transformation, and model interpretability; Computational accounts of object and scene representations; and testing alignment between artificial and biological vision systems. The candidate will have full access to newly acquired local GPU and CPU computational infrastructure, as well as institutional high-performance computing facilities. A central component of the position will involve leveraging human neuroimaging data — particularly fMRI (3T, with potential access to 7T) — to test and constrain computational models of visual representation and generative systems. We are especially interested in researchers aiming to establish an independent research group (seed funding may be available) and who adopt a strong fundamental science perspective grounded in cognitive science and systems neuroscience. The ideal candidate will integrate large-scale modeling approaches with empirical neural data to advance mechanistic accounts of visual cognition. The position offers rich collaborative opportunities with faculty in cognitive neuroscience and perception, including Jorge Almeida, Joana Carvalho, Jason Fischer, Pawel Tacikowski, Robert Whitwell, Zohar Tal, Ceren Battal, and Alfonso Caramazza. The department provides access to 3T MRI facilities (with potential 7T collaborations), TMS (Magstim Super Rapid² with Brainsight neuronavigation), EEG, tDCS, eye tracking, motion tracking, and virtual reality systems. This position is embedded within the major European-funded CogBooster initiative, which aims to establish a permanent and internationally competitive team of cognitive neuroscientists at the Department of Psychology of the University of Coimbra. The current group is highly international and conducts research spanning low- and high-level vision, memory, attention, action, and conceptual processing. Researchers with strong backgrounds in machine learning, computer vision, or computational neuroscience — and a genuine interest in linking artificial systems to biological cognition — are particularly encouraged to apply. If you — or someone you know — might be interested in this opportunity, please feel free to contact me. Best regards, Jorge

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Independent young PI Position to lead the newly formed TMS Lab (University of Coimbra)
by Jorge Almeida March 2, 2026

March 2, 2026

Dear all, Apologies for the cross-posting. We are seeking a researcher interested in establishing themselves as an independent PI under a 2–3 year contract in Portugal, to lead and develop a newly established TMS laboratory. The lab is equipped with a Magstim Super Rapid² TMS system and Brainsight neuronavigation. The successful candidate will lead the TMS facility and will have substantial access to the equipment to pursue their own research program. There will also be opportunities for collaboration with other PIs in the department, including Jorge Almeida, Joana Carvalho, Jason Fischer, Pawel Tacikowski, Robert Whitwell, Zohar Tal, Ceren Battal, and Alfonso Caramazza, on joint TMS projects. We are particularly interested in researchers who aim to build an independent research group (seed funding may be available, as well as support for major competitive applications - e.g., ERC) and who use TMS to investigate cognitive processes from a fundamental science perspective. While our primary focus is basic cognitive neuroscience, we would consider work involving neurological populations (e.g., individuals with brain damage), though we are less focused on psychiatric or clinical intervention approaches. In addition to TMS, the department provides access to tDCS systems, fMRI, EEG, fMRI compatible EEG, eye tracking, motion tracking, and virtual reality equipment. This position is part of the major European-funded CogBooster initiative, which aims to establish a permanent team of cognitive neuroscientists within the Department of Psychology at the University of Coimbra. The current group is highly international and conducts research spanning low- and high-level vision, memory, attention, and action. If you — or someone you know — might be interested in this opportunity, please feel free to contact me. Best regards, Jorge

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Independent young PI Position to lead the EEG Lab (University of Coimbra)
by Jorge Almeida March 2, 2026

March 2, 2026

Dear all, Apologies for the cross-posting. We are seeking a researcher interested in establishing themselves as an independent PI under a 2–3 year contract in Portugal, to lead and further develop our EEG laboratory (256 Ch EEG AntNeuro) and a newly purchased fMRI compatible EEG to be used in our 3T MRI scanner. The successful candidate will oversee the EEG facility and will have substantial access to the equipment to pursue their own research program. There will also be opportunities for collaboration with other PIs in the department, including Jorge Almeida, Joana Carvalho, Jason Fischer, Pawel Tacikowski, Robert Whitwell, Zohar Tal, Ceren Battal, and Alfonso Caramazza, on joint EEG projects. Moreover, the PI will co-lead with Joana Carvalho the use of the fMRI compatible EEG to be used in the 3T MRI scanner. We are particularly interested in researchers who aim to build an independent research group (seed funding may be available) and who use EEG to investigate major cognitive processes from a fundamental science perspective and from a strong Cognitive Science perspective. In addition to EEG, the department provides access to TMS (Magstim Super Rapid² with Brainsight neuronavigation), tDCS systems, fMRI, eye tracking, motion tracking, and virtual reality equipment. This position is part of the major European-funded CogBooster initiative, which aims to establish a permanent team of cognitive neuroscientists within the Department of Psychology at the University of Coimbra. The current group is highly international and conducts research spanning low- and high-level vision, memory, attention, and action. If you — or someone you know — might be interested in this opportunity, please feel free to contact me. Best regards, Jorge

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[Reminder] BAMB! 2026 Applications close March 6 (Friday)
by manuel molano March 2, 2026

March 2, 2026

Dear colleagues, This is a final reminder that applications for the 6th edition of the Barcelona Summer School for Advanced Modeling of Behavior (BAMB! 2026) close this Friday, March 6th (midnight Pacific Time). Apply at: www.bambschool.org<http://www.bambschool.org> BAMB! 2026 (July 12–23) is an intensive two-week program in Barcelona for PhD students and postdocs in cognitive and computational neuroscience. Quick Highlights: * Curriculum: Reformulated for 2026 to cover the latest in Neuro-AI and behavioral modeling. * Format: Lectures, hands-on tutorials, and collaborative group projects. * Grants: Travel and childcare grants are available. * Keynotes: Megan Peters (UCI) and Marcelo Mattar (NYU). Don't miss the chance to join us in Barcelona this summer to master the art of modeling behavior! Best regards, The BAMB! 2026 Organizing Team info(a)bambschool.org

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Multiple PhD positions in Cognitive Neuroscience at SISSA - application deadline 20 March 2026
by Eugenio Piasini March 2, 2026

March 2, 2026

Up to 6 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2026. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience group (https://phdcns.sissa.it/) hosts 7 research labs that study the neuronal bases of time and magnitude processing, neuronal foundations of perceptual experience and learning in various sensory modalities, motivation and intelligence, language, and neural computation. Our research is highly interdisciplinary; our approaches include behavioral, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply. The selection procedure is now open; please apply here (https://pica.cineca.it/sissa/phd-cns-01-2026/) The application deadline for the spring admission round is 20 March 2026 at 1pm CET. For more information on the procedure, see our admissions page (https://phdcns.sissa.it/admission-procedure) Please contact the PhD Coordinator Mathew Diamond (diamond(a)sissa.it) and/or your prospective supervisor for more information and informal inquiries.

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Postdoctoral Researcher in AI for Mental Health — AHA Lab @ CUHK‑Shenzhen
by Prof. Sahba ZOJAJI (HSS) March 2, 2026

March 2, 2026

Position: Postdoctoral Researcher (1 position) Lab/Unit: Augmented Humanity & AI (AHA) Lab, School of Artificial Intelligence (SAI), The Chinese University of Hong Kong, Shenzhen (CUHK‑Shenzhen) Location: Shenzhen, China | On‑site Term: 24 months, renewable based on performance and funding Start date: Flexible (target window: by June 2026) Summary The AHA Lab at CUHK‑Shenzhen invites applications for postdoctoral researchers in Artificial Intelligence for Mental Health and Therapy, with emphasis on multimodal LLMs that fuse language, facial, vocal, and physiological signals, social robotics for real-time affective interaction, and VR-based exposure and skills-training environments—all targeting transparent, trustworthy support for CBT and other evidence-based interventions. The postdoc will drive independent and collaborative research, publish at CHI/ACL/EMNLP/AAAI/ICLR/NeurIPS/TAC, and mentor students in a fast‑growing AI school. Responsibilities - Design and fine-tune multimodal LLMs that integrate language, facial, vocal and physiological data for real-time inference of affect, risk and therapeutic needs. - Develop social-robotic and VR prototypes that deliver CBT micro-interventions, exposure tasks or skills coaching, and evaluate usability and efficacy in lab and clinical settings. - Create interpretable, uncertainty-aware models that supply clinicians and patients with actionable explanations; ensure compliance with ethical, privacy and regulatory standards. - Collect, curate and analyze multimodal datasets (wearables, EEG/fMRI, smartphone logs, robot interaction logs, VR telemetry). - Lead papers and presentations at top venues; contribute to grant writing and open‑source releases. - Co‑supervise graduate students; collaborate within CUHK‑Shenzhen and external partners. Required qualifications - Ph.D. in Computer Science/AI/Computational Psychology, or related field by start date. - Strong publication record in top-tier AI conferences in HCI/NLP/ML/Digital Mental Health. - Familiarity with robot operating systems (ROS/ROS2) or Game engines (Unity/Unreal). - Proficiency with modern Deep Learning toolchains and NLP frameworks. - Ability to design and run human-subject studies. - Excellent scientific communication in English. - At the time of contract commencement, the age must be under 35. Preferred qualifications - Experience with Clinical multimodal data, mental-health metrics, CBT/DBT training or certification, Explainable/uncertain AI, federated/privacy learning, affect-driven robot dialogue or VR therapy. - Project leadership and mentoring experience. Compensation & benefits Competitive salary commensurate with experience; benefits per CUHK‑Shenzhen policy; travel support to major conferences. Application Submit as a single PDF: (1) CV with publications; (2) 1–2 page research statement; (3) up to 3 representative papers; (4) names/emails of 2–3 referees. Review begins: 15 March 2026; applications accepted until filled. Contact: sukiwang(a)cuhk.edu.cn<mailto:sukiwang@cuhk.edu.cn>

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Postdoctoral Researcher in Speech & Language Processing — SLC Lab @ CUHK Shenzhen
by Prof. Sahba ZOJAJI (HSS) March 2, 2026

March 2, 2026

Position: Postdoctoral Researcher (1 position) Lab/Unit: Spoken Language Communication (SLC) Lab, School of Artificial Intelligence (SAI), The Chinese University of Hong Kong, Shenzhen (CUHK‑Shenzhen) Location: Shenzhen, China | On‑site Term: 24 months, renewable based on performance and funding Start date: Flexible (target window: by June 2026) Summary The SLC Lab at CUHK‑Shenzhen invites applications for postdoctoral researchers in speech and language processing, with emphasis on simultaneous speech translation (SimulST), LLM‑based S2T/S2ST, simultaneous policy learning (RL/DPO), streaming alignment (CIF/monotonic attention/Transducer), and multimodal LLMs. The postdoc will drive independent and collaborative research, publish at ACL/EMNLP/AAAI/ICLR/ICASSP/INTERSPEECH//TASLP, and mentor students in a fast‑growing AI school. Responsibilities - Design/execute research on SimulST, streaming ASR/ST, S2ST, and multimodal LLMs. - Develop reproducible pipelines (PyTorch/JAX; Hugging Face; ESPnet/Fairseq/NeMo/SpeechBrain). - Lead papers and presentations at top venues; contribute to grant writing and open‑source releases. - Co‑supervise graduate students; collaborate within CUHK‑Shenzhen and external partners. Required qualifications - Ph.D. in EE/CS/Computational Linguistics or related field by start date. - Strong publication record in top-tier AI conferences in speech/NLP/ML. - Proficiency with modern DL toolchains and speech/NLP frameworks. - Excellent scientific communication in English. - At the time of contract commencement, the age must be under 35. Preferred qualifications - Experience with SimulST/read–write policies, RL/DPO/RLHF, CIF/CTC/monotonic attention, multilingual and low‑resource speech, evaluation (COMET, AL/ATD, WER/TER), robustness/accent/noise, prosody/voice, machine speech chain, and large‑scale training. - Project leadership and mentoring experience. Compensation & benefits Competitive salary commensurate with experience; benefits per CUHK‑Shenzhen policy; travel support to major conferences. Application Submit as a single PDF: (1) CV with publications; (2) 1–2 page research statement; (3) up to 3 representative papers; (4) names/emails of 2–3 referees. Review begins: 15 March 2026; applications accepted until filled. Contact: snakamura(a)cuhk.edu.cn<mailto:snakamura@cuhk.edu.cn>

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World wide VVTNS series (6th season): Unsupervised representation learning by amortised neural message-passing | Wednesday, March 4, 2026, at 11:00 am ET - Lior Fox, Gatsby Computational Neuroscience Unit
by David Hansel March 1, 2026

March 1, 2026

[image: VVTNS.png] https://www.wwtns.online <https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.on…> - on twitter: wwtns@TheoreticalWide You are cordially invited to the lecture Lior Fox Gatsby Computational Neuroscience Unit on the topic of * Unsupervised representation learning by amortised neural * *message-passing* The lecture will be held on zoom on March 4 ,2026, at *11:00 am ET * > To receive the link: https://www.wwtns.online/register-page > *Abstract: *Useful internal representations should explain the patterns of regularities and dependencies among observations. Probabilistic graphical models promise a principled way to uncover latent factors as such, but they are hard to scale to handle high-dimensional sensory observations and complicated dependency structures. Neural-networks, on the other hand, excel at approximating complicated high-dimensional functions, but their internal representations do not easily lend themselves to a probabilistic interpretation. Despite some successes, a general unified approach is still missing for integrating the two approaches. I will describe a novel approach towards merging adaptive neural-network components into a probabilistic framework, based on three core ideas. The first is to train a set of networks to collectively perform inference, leveraging the ability of pattern-recognition methods to amortise complicated transformations. The second is to constrain the way in which the outputs of these networks are interpreted, transformed, and combined together. These constraints, together with the learning objective itself, are derived directly from probabilistic considerations encoded in a graphical model. Finally, the third core idea is that of recognition-parameterization, allowing the inference ("recognition") procedure to directly define the model itself, without requiring an explicit "generative" decoder. *About VVTNS : Launched as the World Wide Theoretical Neuroscience Seminar (WWTNS) in November 2020 and renamed in homage to Carl van Vreeswijk in Memoriam (April 20, 2022), Speakers have the occasion to talk about theoretical aspects of their work which cannot be discussed in a setting where the majority of the audience consists of experimentalists. The seminars, **held on Wednesdays at 11 am ET,** are 45-50 min long followed by a discussion. The talks are recorded with authorization of the speaker and are available to everybody on our YouTube channel.* ᐧ ᐧ ᐧ

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ICIST 2026 – Call for Papers
by Nian Ashlee Zhang Feb. 28, 2026

Feb. 28, 2026

The 16th International Conference on Information Science and Technology (ICIST 2026) will be held in Coimbra, Portugal, October 1-6, 2026. Topics include intelligent control and automation, artificial intelligence, machine learning, information processing, computer vision, networking, and information security. Submission deadline: May 15, 2026. Technically supported by IEEE SMC. Accepted papers are expected to be included in IEEE Xplore. Conference website & submission: https://conference.cs.cityu.edu.hk/icist/ Please feel free to share with interested colleagues.

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Accepting applications: The Brain Prize Course - Computational and Theoretical Neuroscience 2026
by Maria Neimark Geffen Feb. 27, 2026

Feb. 27, 2026

Dear Colleagues, I’m writing as a co-director of the upcoming *CAJAL Advanced Neuroscience Training Course: The Brain Prize Course - Computational and Theoretical Neuroscience 2026*, now open for applications. This intensive course is a unique opportunity for early-career researchers interested in this field. Hosted at the *Champalimaud Centre for the Unknown*, this three-week course focuses on *bridging the gap between theoretical frameworks and experimental neuroscience*. It provides intensive training in neural coding, network dynamics, and advanced data analysis, combining high-level lectures with hands-on Python-based projects to model complex brain functions and interpret large-scale neuronal datasets. 📍 *Location:* Champalimaud Centre for the Unknown, Lisbon, Portugal 📅 *Dates:* 13 - 31 July 2026 ⏳ *Application Deadline:* 27 February 2026 🔗 *Apply now:* https://form.jotform.com/Cajal_training/computational-neuro2026 *Course Directors* - *Alfonso Renart* - Champalimaud Foundation, Portugal - *Julijana Gjorgjieva* - Technical University of Munich, Germany - *Maria Geffen* - University of Pennsylvania, USA - *Omri Barak* - Technion, Israel Institute of Technology *Course Themes & Topics:* - *Neural Modeling & Dynamics*: Mastering the mathematical foundations of brain function, from single-cell biophysical models like Hodgkin−Huxley to the complex behavior of excitatory-inhibitory networks. - *Neural Coding & Data Analysis*: Learning how populations of neurons represent information using advanced computational tools like dimensionality reduction to decode large-scale neural datasets. - *Cognitive & Normative Theory*: Exploring high-level functions through Bayesian inference and reinforcement learning to understand how the brain optimizes decision-making and motor control. *Speakers include:* - Larry Abbott (Columbia University, USA) - Haim Sompolinsky (Harvard University, USA & Hebrew University of Jerusalem, Israel) - Susanne Schreiber (Humboldt University of Berlin, Germany) - Francesca Mastrogiuseppe (SISSA – International School for Advanced Studies, Italy ) - Il Memming Park (Champalimaud Foundation, Portugal) - Jakob Macke (University of Tübingen, Germany ) - Wiktor Młynarski (LMU Munich – Ludwig-Maximilians-Universität München, Germany ) - Laura Busse (LMU Munich – Ludwig-Maximilians-Universität München, Germany ) - Yiota Poirazi (IMBB–FORTH, Heraklion, Greece ) - Adrienne Fairhall (University of Washington, USA) - Agostina Palmigiano (Gatsby Computational Neuroscience Unit, UCL, UK) - Joe Paton (Champalimaud Foundation, Portugal) - Dmitri Chklovskii (Flatiron Institute, Simons Foundation, USA) - Brent Doiron (University of Chicago, USA) - Gilles Laurent (Max Planck Institute for Brain Research, Germany) - Alex Cayco Gajic (École Normale Supérieure – PSL, France ) - Rafal Bogacz (MRC Brain Network Dynamics Unit, University of Oxford, UK) ________________________________ Maria Neimark Geffen, Ph.D. (she/her) Professor Co-director, Penn Computational Neuroscience Initiative Department of Otorhinolaryngology: HNS (primary) Department of Neuroscience Department of Neurology Perelman School of Medicine University of Pennsylvania 5 Ravdin 3400 Spruce St. Philadelphia PA 19104 Tel.: 215.898.0782 My zoom <https://pennmedicine.zoom.us/my/mgeffen?pwd=eWVFbUJFUGRJZ3g1eU9YZEZic2VwZz09> mgeffen(a)pennmedicine.upenn.edu http://hosting.med.upenn.edu/hearing https://bsky.app/profile/geffenlab.bsky.social

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CfP AISC 2026 - Natural and Artificial Intelligence: between Skills and Biases
by Luca Tummolini Feb. 27, 2026

Feb. 27, 2026

*Call for Papers — AISC 2026* *https://www.aisc2026.com/ <https://www.aisc2026.com/>* *22nd Annual Conference of the Italian Association for Cognitive Science:* *Natural and Artificial Intelligence: between Skills and Biases* Torino, Italy — *September 9–11, 2026* The *Italian Association for Cognitive Science (AISC)* invites submissions for AISC 2026, an interdisciplinary forum for researchers in all areas of cognitive science. We particularly encourage submissions addressing skills and biases in both natural and artificial intelligent systems, but we welcome contributions from all areas of cognitive science, including theoretical, empirical, computational, philosophical, and interdisciplinary work that advances understanding of intelligence across biological, social, and artificial domains. ------------------------------ *IMPORTANT DATES* - *Deadline for Abstract Submission: 20 April 2026* - *Notification of Acceptance: 22 June 2026* - *Conference Dates: 9–11 September 2026* ------------------------------ *INVITED SPEAKERS* - *Francesca Garbarini *(University of Turin) - *Marco Marini* (ISTC-CNR – Winner of the AISC Early Career prize 2025) - *Carlotta Pavese* (University of Oxford) - *Walter Quattrociocchi* (Sapienza University of Rome) ------------------------------ Abstracts for talk or poster presentation (500 words references excluded, anonymized), or for symposia (2000 words, references excluded, with author details) must be submitted at the following link: *https://cmt3.research.microsoft.com/IACS2026/Submission/Index <https://cmt3.research.microsoft.com/IACS2026/Submission/Index>* For any questions about the submission process, please visit *https://www.aisc2026.com/calls-committees <https://www.aisc2026.com/calls-committees>* or reach out via the following email address: *aisc2026(a)outlook.com <aisc2026(a)outlook.com>* ------------------------------ TOPICS OF INTEREST: We accept submissions on all topics related to cognitive science, including (but not limited to): 4E cognition – artificial intelligence and cognition – bounded rationality and decision-making – cognitive architecture – cognitive biases and heuristics – cognitive modeling – cognitive neuroscience – cognitive psychology – cognitive and motor skills – cognitive robotics – collective intelligence and collective behavior – computational linguistics – computational social sciences – decision-making under uncertainty – deep neural models – developmental science – embodied and grounded cognition – expertise and know-how – epistemology – ethics of artificial intelligence and robotics – history of cognitive science – human–AI interaction – learning and adaptation in natural and artificial systems – natural and artificial consciousness – neuroethics – neurolinguistics – philosophy of biology – philosophy of cognitive science – philosophy of language – philosophy of mind – philosophy of neuroscience – philosophy of health and medicine – philosophy of technology – psycholinguistics – social cognition and social influence – social robotics – misinformation and information dynamics in networks ------------------------------ PROGRAM COMMITTEE - Fabrizio Calzavarini, University of Torino - Vincenzo Crupi, University of Torino - Marco Viola, Roma Tre University ORGANIZING COMMITTEE - Alessandro Demichelis, IMT School for Advanced Studies Lucca - Head of committee - Maddalena Costa, University of Torino - Alessandra Civani, University of Pavia - Lorenzo D'Errico, University of Torino - Niccolò Nanni, University of Torino SCIENTIFIC COMMITTEE - Carola Barbero (University of Torino) - Francesco Bianchini (University of Bologna) - Monica Bucciarelli (University of Torino) - Fabrizio Calzavarini (University of Torino) - Vincenzo Crupi (University of Torino) - Olga Dal Monte (University of Torino) - Federico D'Agata (University of Torino) - Edoardo Datteri (University Milano-Bicocca) - Sara Dellantonio (University of Trento) - Alessandro Demichelis (IMT School for Advanced Studies Lucca) - Elvira Di Bona (University of Torino) - Matteo Diano (University of Torino) - Francesco Ianì (University of Torino) - Antonio Lieto (University of Salerno) - Chiara Lucifora (LINK Campus University) - Claudia Mazzuca (Sapienza University of Rome) - Eugenio Petrovich (University of Torino) - Antonella Tramacere (University Roma Tre) - Luca Tummolini (ISTC-CNR) - Marco Viola (Roma Tre University) - Alberto Voltolini (University of Torino) -- ==================================== Luca Tummolini Istituto di Scienze e Tecnologia della Cognizione - CNR Via Giandomenico Romagnosi 18A - 00196 - Roma Personal Website: http://www.istc.cnr.it/people/luca-tummolini Lab Website: http://www.istc.cnr.it/group/mind Office: +39 06 44595 272 E-mail: luca.tummolini(a)istc.cnr.it

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Research Fellow - Deep Learning Theory & AI Safety
by I-Chun Lin Feb. 26, 2026

Feb. 26, 2026

Closing date: 26 March 2026 We invite applications for a postdoctoral training fellowship under the guidance of Professor Andrew Saxe. The Saxe Lab works across the Gatsby Computational Neuroscience Unit and the Sainsbury Wellcome Centre and is focussed on understanding learning in biological and artificial systems. This role lies at the interface of deep learning theory and AI safety. You will conduct research into artificial deep networks using techniques from applied mathematics and statistical physics. A particular focus is on understanding how depth affects learned representations and network behaviour, with applications to AI safety including emergent misalignment, unlearning and the efficacy of safety fine-tuning. The project is a collaboration with the Sarao Mannelli group at Chalmers University of Technology, where additional Research Fellows will be based. Funding for collaborative visits is included. This post is funded for two years by a grant from OpenAI's Alignment Team, awarded by the UK AI Security Institute, through the Alignment Project. The appointment will be on the UCL Grade 7 salary scale £45,103 - £52,586. Grade 7 Research Fellows will also be eligible for a departmental allowance of up to £5,000 p.a. You should have a PhD in Computer Science, Physics or closely related discipline (Engineering, Theoretical Neuroscience, Mathematics) or have submitted your final thesis by the agreed start date of the position. A proven track-record of publishing work as a lead author relating to theory of deep learning or AI safety is essential as is a demonstrable interest in mathematical analysis of artificial neural network models. A demonstrable interest in AI safety and track record of running controlled empirical experiments on large deep network models on HPC or via frontier model APIs is desirable. For detailed information on the role and how to apply, please visit “UCL Job: B02-10169” (https://www.ucl.ac.uk/work-at-ucl/search-ucl-jobs/details?jobId=42098&jobTi…)

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