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9th Advanced Course on Data Science & Machine Learning - ACDL 2026 - From AI Agents to Agentic AI | June 8-12 | Riva del Sole Resort & SPA - Italy -> ⏰ Early Registration: by 23 March

by ICAS Organizing Committee

* Apologies for multiple copies. Please forward the course information among anybody (labs/departments/students) and social and scientific networks that might be interested. Thank you! * 🎓 ACDL 2026, A Residential Course: From AI Agents to Agentic AI, 📆 June 8-12, Tuscany ⏰ Early Registration: by 23 March 📚 ACDL 2026 is a full-immersion 5-day course in Riva del Sole Resort & SPA - Tuscany on cutting-edge advances in AI, Deep Learning, LLMs, Data Sc., Generative AI, AI Agents & Agentic AI with lectures delivered by world-renowned experts. a 📍Riva del Sole Resort & SPA - Tuscany, June 8-12, 2026 🔗 https://acdl2026.icas.events 📧 acdl(a)icas.cc ⏰ EARLY REGISTRATION: by March 23 (AoE) ⏳ ➡️ Register here: https://acdl2026.icas.events/registration/ 📝 LECTURERS: Each Lecturer will hold up to four lectures on one or more research topics. 🔗 https://acdl2026.icas.events/lecturers/ It follows the (partial) list of the confirmed 👩‍🏫 ACDL 2026 Lecturers: Lucas Beyer, Meta, Zürich, Switzerland Sven Giesselbach, T Systems International & Fraunhofer Institute - IAIS, Germany Anirudh Goyal, Google DeepMind & Mila, Université de Montréal, Canada Arthur Gretton, UCL & Google DeepMind, UK Katja Hofmann, Microsoft, UK Arnulf Jentzen, University of Münster, Germany Pushmeet Kohli, Google DeepMind, UK Panos Pardalos, University of Florida, USA Michal Valko, Stealth Startup, INRIA, MVA at ENS Paris-Saclay, France David van Dijk, Yale University, USA Jason Weston, META, USA More Lecturers TBA 📑 LECTURES: 🔗 https://acdl2026.icas.events/lectures/ 📚 PAST LECTURERS: 🔗 https://acdl2026.icas.events/past-lecturers/ ✅ VENUE: 🔗 https://acdl2026.icas.events/venue/ Riva del Sole Resort & SPA Località Riva del Sole‚ Castiglione della Pescaia (Grosseto) CAP 58043‚ Tuscany‚ Italy p: +39-0564-928111 📧 booking.events(a)rivadelsole.it 🔗 https://www.rivadelsole.it/en/ PAST EDITIONS: 🔗 https://acdl2026.icas.events/past-editions/ 📚 CERTIFICATE & 8 ECTS: The 9th Advanced Course on Data Science & Machine Learning – ACDL 2026 is a full-immersion five-day Course at the Riva del Sole Resort & SPA (Castiglione della Pescaia – Grosseto – Tuscany, Italy) on cutting-edge advances in Deep Learning, Data Science and Generative AI with lectures delivered by world-renowned experts. The Course provides a stimulating environment for PhD students, Post-Docs, junior academics (only up to assistant professors), early career researchers, and industry leaders (and highly motivated, promising and brilliant Master students / BSc students). Participants will also have the chance to present their results with talks, and to interact with their colleagues, in a convivial, professional and productive environment. PhD students, PostDocs, Industry Practitioners and Junior Academics (only up to assistant professors) will be typical profiles of the ACDL attendants.The Course will involve a total of 36–40 hours of lectures, according to the academic system the final achievement will be equivalent to 8 ECTS points for the PhD Students (and some strongly motivated Master Student – BSc Student) attending the Course. Language: English. To participate in the ACDL 2026, all attendants must (1/2) register for the course (by March 23, 2026) and (2/2) book accommodation at the course venue, “Riva del Sole Resort & SPA” (by April 23, 2026); all attendants must stay at the “Riva del Sole Resort & SPA”. Booking accommodation at the Riva del Sole must be made exclusively using the accommodation form attached to the registration confirmation email. No other methods must be used (if you use other booking methods the hotel will cancel the reservation). Finally, it is not possible to extend the stay, the special accommodation rates are valid only for the period of the course, no exceptions will be made. To contact the Riva del Sole Resort & SPA (the course venue) use the following email: booking.events(a)rivadelsole.it Once accommodation has been booked at “Riva del Sole Resort & SPA“, the participant must send this information (including the Booking Number) to the ACDL organizing committee (acdl(a)icas.cc) ACDL is a residential course, so all lecturers and participants must reside in the same Hotel (Riva del Sole Resort & SPA). No exceptions are made. For privacy reasons, the Hotel can not match people. If you have someone to share the apartment with (or a double room in Hotel), please send to the Hotel ( 📧 booking.events(a)rivadelsole.it ) the name, surname and email address. Otherwise the solution is to book a hotel room in single use. Please note that only ACDL registered participants can book a room (in hotel or apartment) at the Riva del Sole Resort & SPA with the accommodation form attached in the registration email and with the ACDL Discounted Rates. The Booking Office of the Riva del Sole Resort & SPA will verify the names of the participants and the corresponding registration number to confirm the booking. Anyone interested in participating in ACDL 2026 should register as soon as possible. See you in Riva del Sole in June! ACDL 2026 Directors. ACDL 2026 Scientific & Organizing Committees. 🔗 https://acdl2026.icas.events 📧 acdl(a)icas.cc Obviously this is only a Call for Participation, to have complete and updated information we recommend you access the relevant website: 🔗 https://acdl2026.icas.events * Apologies for multiple copies. Please forward to anybody who might be interested, thanks * #MachineLearning #AI #SummerSchool #Research #DeepLearning #DataScience

March 9, 2026

Postdoctoral Researcher in visual cognitive computational neuroscience at the University of Cambridge (Kamila Maria Jozwik)

by Kamila Maria Jóźwik

*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.

March 8, 2026

PhD position in visual cognitive computational neuroscience at the University of Cambridge (Kamila Maria Jozwik)

by Kamila Maria Jóźwik

*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.

March 8, 2026

Opportunity for postdocs and senior grad students

by Goodhill, Geoffrey

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

March 8, 2026

DeepLearn 2026: early registration March 29

by David Silva - IRDTA

****************************************************** 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

March 7, 2026

🧠[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

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

March 6, 2026

WashU CTCN Postdoc Fellows

by Goodhill, Geoffrey

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

March 5, 2026

NEST Conference 2026 Abstract Submission and Registration open

by Hans Ekkehard Plesser

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

March 5, 2026

Study Computational & AI-Centered Cognitive Science in Vienna

by Moritz Grosse-Wentrup

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

March 5, 2026

Neural Data Analysis Workshop before FENS forum (Jun 3-4, 2026)

by Edoardo Balzani

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

March 4, 2026