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GeMAIHc 2027: early registration September 18
by David Silva - IRDTA Aug. 29, 2026

Aug. 29, 2026

******************************************************** 1st INTERNATIONAL SCHOOL ON GENERATIVE & MULTIMODAL AI FOR HEALTHCARE GeMAIHc 2027 Milan, Italy February 1-5, 2027 https://gemaihc.irdta.eu/2027/ ******************************************************** Co-organized by: Human Technopole IRDTA – Institute for Research Development, Training and Advice ****************************************************** Early registration: September 18, 2026 ****************************************************** SCOPE AI is rapidly redefining biomedical research, clinical decision-making, and healthcare delivery worldwide. GeMAIHc 2027 will provide participants with a comprehensive overview of state-of-the-art AI methodologies. Topics will include generative modelling, multimodal data integration and augmentation, large language models and clinical NLP, medical image synthesis and analysis, as well as evaluation, robustness, validation, ethical considerations, and regulatory aspects of AI systems in healthcare. GeMAIHc 2027 aims to become a leading international forum at the intersection of generative AI, multimodal learning, and healthcare innovation. The program will emphasize both methodological foundations and real-world clinical applications, combining theoretical insights with practical perspectives. This interdisciplinary event will feature 18 monographic three-hour courses, 2 keynote lectures, 3 scientific sessions, 1 symposium, and a hackathon. Leading academics and industry pioneers will share their expertise and perspectives with attendees. In-person interaction and networking will be central components of the event, while full remote participation will also be possible. ADDRESSED TO The program is open to PhD students and postdoctoral researchers in AI, data science, and biomedical disciplines, clinicians and healthcare professionals, industry practitioners and innovators, and policymakers and stakeholders in digital health. There are no formal academic prerequisites for participation. Researchers and professionals at all career stages are welcome. An international audience is expected, with backgrounds spanning computer science, medicine, engineering, mathematics, statistics, physics, and the social sciences. GeMAIHc 2027 will offer a unique opportunity to gain in-depth knowledge of a rapidly evolving field, to interact with experts across disciplines, to establish collaborations and expand professional networks, and to explore how AI is reshaping the future of healthcare. VENUE GeMAIHc 2027 will take place in Milan, one of Europe’s leading international centers for science, industry, fashion, and finance. The venue will be: Human Technopole Viale Rita Levi-Montalcini, 1 20157 Milan, Italy https://humantechnopole.it/en/ STRUCTURE Three parallel courses will run throughout the event. Participants will be free to choose the sessions they wish to attend and may move between courses at any time. A symposium will offer participants and companies the opportunity to present ongoing research or industrial developments through 10-minute oral presentations. The school will include a hackathon, during which participants will work in teams to tackle challenges in generative and multimodal AI for healthcare. All lectures will be video recorded and made available to participants for 45 days after the event. Full live online participation will be possible. Nevertheless, the organizers emphasize the importance of in-person interaction and networking in research training events of this kind. KEYNOTE SPEAKERS Deborah Estrin (Cornell University), Transforming Longitudinal Care with Digital Biomarkers and Therapeutics Lyle Palmer (Adelaide University), Deep Learning in Medicine: Some Lessons from Epidemiology and Genomics PROFESSORS AND COURSES Pierre Baldi (University of California Irvine), [introductory/advanced] The AI-driven Healthcare of the Future David Buckeridge (McGill University), [intermediate] Multi-Modal Data Analysis in Population and Public Health Alejandro Frangi (University of Manchester), [intermediate/advanced] Virtual Patient Populations for In Silico Trials Using Generative AI Based on Multimodal Real-World Data Charles Friedman (University of Michigan), [introductory] Synergizing AI and Learning Health Systems to Transform Health Judy Wawira Gichoya (Emory University), [introductory/advanced] Combination in a Hands on Lab for Harmonizing Radiology Datasets Maryellen Giger (University of Chicago), [introductory/intermediate] Role of Data & Algorithms in Trustworthy Medical Imaging AI Casey Greene (University of Colorado), [introductory/advanced] Translating Generative AI into Healthcare from Pharmacogenomics to Foundation Models Tina Hernandez-Boussard (Stanford University), [intermediate/advanced] The AI Lifecycle in Healthcare: Evaluation, Deployment, and Responsible Implementation Jianying Hu (IBM Thomas J. Watson Research Center), [intermediate/advanced] Advanced Computing for Biomedical Research and Discovery Jayashree Kalpathy-Cramer (University of Colorado), [introductory] Toward Digital Twins: Multimodal AI for Imaging and Precision Medicine Alex John London (Carnegie Mellon University), [intermediate/advanced] Ethical and Scientific Challenges to Unlocking the Clinical Value of Artificial Intelligence Asoke Nandi (Brunel University of London), [introductory/advanced] Selected Case Studies of Medical Image Segmentation Tom Pollard (Massachusetts Institute of Technology), [introductory/intermediate] Data: The Foundation, Constraint, and Failure Mode of Deployable Health AI Hoifung Poon (Recursion), [advanced] Toward Virtual Patient: AI for Accelerating Medical Discovery Jian Tang (Mila-Québec AI Institute), [introductory/advanced] Generative AI for Protein Design Peter van Ooijen (University of Groningen), [intermediate] Multimodal AI for Adaptive Radiotherapy: Tumor Segmentation, Uncertainty, and Explainability Karin Verspoor (Royal Melbourne Institute of Technology), [introductory/intermediate] AI Scientists and beyond: Roles for GenAI in Biomedical Research and Discovery Jelmer M. Wolterink (University of Twente), [intermediate/advanced] Data Representations in Health Digital Twinning: From Features to Foundation Models HT RESEARCH PERSPECTIVES Human Technopole will organize 3 scientific sessions: Probabilistic Models for (Medical) Image Analysis, by Jan Funke, Florian Jug, Federico Carrara, and Benjamin Salmon AI Models for Computational Biology, by Andrea Sottoriva, Michele Calabrò, and Manuel Dileo AI in Health Data Science, by Francesca Ieva, Andrea Ganna, Michela Carlotta Massi, Nicole Fontana, and Alessia Mapelli SYMPOSIUM A symposium will feature voluntary 10-minute oral presentations on ongoing research projects and industrial developments. Participants interested in presenting should submit a one-page abstract including title, authors, and summary to david(a)irdta.eu by January 8, 2027. HACKATHON Hands-on team activities will be organized around challenges in generative and multimodal AI for healthcare. The challenges will be released two weeks before the beginning of the school. A jury will evaluate the submissions, and the winners will be announced at the end of February 2027. Winning teams will receive a modest monetary prize, while runners-up will receive certificates of recognition. ORGANIZING COMMITTEE Jan Funke (Milan) Ilaria Guerini (Milan) Francesca Ieva (Milan) Carlos Martín-Vide (Tarragona, program chair) Michela Carlotta Massi (Milan, local chair) Santiago Montes (Tarragona, webpage) Sara Morales (Luxembourg, finances) David Silva (London, organization chair) REGISTRATION Registration is available at: https://gemaihc.irdta.eu/2027/registration/ The selection of six courses requested during registration is tentative and non-binding. This information will help estimate demand for logistical planning purposes. As venue capacity is limited, registrations will be processed on a first-come, first-served basis. Registration will close once capacity has been reached. Early registration is strongly recommended. FEES Registration fees include access to all school activities and lunches. Several early registration deadlines are available, and fees vary depending on the registration period. Fees are identical for on-site and online participation. ACCOMMODATION Accommodation suggestions will be provided in due time at: https://gemaihc.irdta.eu/2027/accommodation/ CERTIFICATE Participants will receive a certificate indicating 40 hours of academic activities. This certificate should be suitable for participants seeking ECTS recognition from their home institutions. SPONSORS Companies, institutions, and organizations interested in sponsoring the event may download the sponsorship leaflet from: https://gemaihc.irdta.eu/2027/sponsors/ QUESTIONS AND FURTHER INFORMATION david(a)irdta.eu ACKNOWLEDGMENTS Human Technopole Universitat Rovira i Virgili IRDTA – Institute for Research Development, Training and Advice

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The NIH BRAIN Theories, Models and Methods Program Application Information
by Mollick, Jessica (NIH/NIDA) [E] Aug. 28, 2026

Aug. 28, 2026

The BRAIN Theories, Models and Methods (TMM) program<https://grants.nih.gov/grants/guide/rfa-files/RFA-DA-27-004.html> (RFA DA 27 004) supports the development and validation of innovative and rigorous theories, models and methods to advance the quantitative and predictive understanding of brain function across scales, including behavior. The program emphasizes tools that develop novel approaches for analyzing, integrating, and interpreting large-scale, complex data emerging from the BRAIN initiative and related efforts, including cell-type specific physiological, anatomical, connectivity, and behavioral data. Applications should focus on one or more of the following: * new or significantly advanced theories of brain function * mechanistic and/or predictive models of neural circuit activity and behavior grounded in empirical data * novel computational or statistical methods for analyzing neural and behavioral datasets.  Tools for analyzing brain activity must: * use data with cellular and sub-second temporal resolution (e.g., single-unit recordings, cellular imaging, connectomics) OR integrate information across multiple, clearly defined temporal scales (e.g., from synaptic events to learning).  * Approaches relying solely on non-invasive, low-resolution signals (e.g., scalp EEG, fMRI BOLD) must be directly integrated with and constrained by cellular/circuit-level data. * Tools for analyzing behavior  must incorporate neural data AND span multiple relevant temporal scales. * Experimental work must be limited to estimating model parameters and/or testing the validity of the TMM tools being delivered.  Theories, models and methods using disease or treatment paradigms are not responsive unless used to understand underlying functional and brain circuits. Important Deadlines: * New submissions: October 6. 2026 * Resubmission/Renewal applications: November 6, 2026 Please send specific aims pages and inquiries to: BRAINTheoriesFOA(a)ninds.nih.gov.<mailto:BRAINTheoriesFOA@ninds.nih.gov> We are happy to consult on fit with the program.

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PhD and Postdoc positions [Neuro-AI lab @ Technion] - Stefano Recanatesi
by Stefano Recanatesi Aug. 28, 2026

Aug. 28, 2026

*PhD and PostDoc Positions Available: Neuro-AI Lab at Technion* *About Us: *Our projects range from developing Brain-Computer Interfaces (BCIs) for both rodents and humans to creating theoretical and computational models of neural activity, and training cutting-edge machine-learning models, including large language models (LLMs). *University Environment: *The Technion offers a world-class research environment with state-of-the-art facilities and a strong international community. The lab is embedded within the Technion’s AI and neuroscience communities and maintains active collaborations with research groups abroad. We strongly encourage international mobility, supporting students in undertaking research visits, internships, and collaborative projects at partner institutions. In light of the current political situation, international students are particularly encouraged to take advantage of these collaborations, including opportunities to spend extended periods working in partner laboratories abroad. *Lab Homepage:* Technion Neuro-AI Lab <https://technionneuroailab.com> *Project Descriptions & Candidate Profiles:* We are seeking candidates with computational/theoretical or experimental expertise in the following areas: - *Computational Projects:* - Focus: Machine learning, artificial intelligence, and training of various network algorithms. - Relevant Backgrounds: Computer Science, Electrical Engineering, Data Science, etc. - *Theoretical Projects:* - Focus: Developing theoretical and computational predictions for neural experiments. - Relevant Backgrounds: Physics, Mathematics, Applied Mathematics, Computer Science, etc. - *Experimental Collaborations:* - Focus: Developing experimental setups with advanced computational tools for BCIs in collaboration with partner labs. - Relevant Backgrounds: Neuroscience, Biology, Medicine, etc., with strong computational skills. *What We Offer:* - A stimulating and supportive research environment. - Access to state-of-the-art equipment and infrastructure. - Opportunities for interdisciplinary scientific development. - An international and collaborative team atmosphere. - Comprehensive training in a wide range of scientific skills. - Opportunities to present your research at international conferences and workshops. - Extensive support for career development beyond your Ph.D. *Application Deadline: *We encourage candidates to apply as soon as possible. Our goal is to fill positions by October 2026. *How to Apply:* Please send your CV and full academic transcript directly to Prof. Stefano Recanatesi (stefano(a)technion.ac.il)

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Postdoctoral Fellow positions available to study spinal mechanisms of motor control
by Levine, Ariel (NIH/NINDS) [E] Aug. 28, 2026

Aug. 28, 2026

Hello Comp-Neuro Community, We have two Postdoctoral Fellow positions available in the lab, where we study how spinal neurons mediate motor control in mice. Next week, our lab will move from NIH to the Krembil Brain Institute at the University Health Network of the University of Toronto. Lab Description We focus on the spinal cord as the final point of neural control over body movement. The spinal cord contains the motoneurons that drive our muscles, the immediate premotor neurons that are the primary regulators of motoneuron activity, and the broader, as-yet-undefined networks of spinal neurons that transform sensory and descending cues into purposeful behavior. We have built extensive atlases of spinal cord cell types and we now want to understand how they act cooperatively to orchestrate movement. Position 1 We are seeking a postdoctoral fellow to investigate whether, and how, cell types shape population activity and mediate specific elements of sensorimotor function. This work will bring together several powerful approaches recently developed and adapted in our lab including in vivo high density electrical recordings from the spinal cords of awake behaving mice, closed-loop optogenetics, spinal cell-type specific manipulation, quantitative behavioral tracking, and computational analysis of neural activity and behavior data (which could involve co-mentorship with an outside collaborator). There is significant opportunity to co-design and lead projects. Position 2 We are seeking a postdoctoral fellow to investigate how specific spinal cord cell types and sensory elements contribute to behavior in mice. This work will also test the links between cell type identify, connectivity, and function by bringing together several powerful approaches including circuit tracing, cell-type specific manipulation, closed-loop optogenetics, and quantitative behavioral tracking. There is significant opportunity to co-design and lead projects. Duties * Development of all stages of research projects) in collaboration with Dr. Levine, members of the laboratory, and outside collaborators * Experimental design, wet-lab procedures, and data analysis * Advise and mentor of students and serve as an active member of laboratory intellectual life * Write and contribute to grant applications * Present research through original research publications, reviews, and talks at scientific conferences Qualifications * Doctoral degree (PhD) must be obtained within the previous 5 years from a recognized * A strong publication record (including pre-prints) * Expertise in one or more of the following areas * Position 1: high density electrophysiology, computational neuroscience, neural circuit tracing and analysis, mouse surgery, and quantitative assessment of sensorimotor behavior * Position 2: viral and anatomical circuit tracing, optogenetics, mouse genetics, mouse surgery, quantitative kinematic analysis, electromyography, and statistical/computational data Applications can be submitted through the UHN SmartRecruiters portal (Position 1<https://jobs.smartrecruiters.com/UniversityHealthNetwork/744000144149159-po…> and 2<https://jobs.smartrecruiters.com/UniversityHealthNetwork/744000144814376-po…>) or through the lab website<http://www.levine-lab.org/>. Thank you for sharing with your students and colleagues. Sincerely, Ariel Ariel Levine, MD, PhD Senior Scientist Peter Gilgan Chair in Neuroregeneration Research Krembil Brain Institute University Health Network University of Toronto www.levine-lab.org<http://www.levine-lab.org>

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Funded PhD Position in NeuroAI and Computational Neuroimaging – University of Rome Tor Vergata
by nicola toschi Aug. 27, 2026

Aug. 27, 2026

Dear Comp-Neuro community, We are recruiting for a three-year funded PhD position in our research group at the University of Rome “Tor Vergata”, within the Italian National PhD Programme in Artificial Intelligence – Health and Life Sciences. We are looking for a highly motivated candidate with a strong quantitative background who is interested in developing advanced AI and computational methods for studying the brain and human health. Depending on the candidate’s interests and expertise, the research may involve one or more of the following areas: * NeuroAI and computational modelling of brain activity * neural foundation models and generative AI * graph machine learning and dynamic brain networks * multimodal neuroimaging, including MRI, PET and CT * EEG, MEG and intracranial electrophysiology * neural decoding and encoding * multimodal representation learning * quantitative biomarkers and personalised prediction * neuromodulation, focused ultrasound and neurotechnology * causal, physical and mathematical modelling of biological systems We particularly welcome candidates with backgrounds in computer science, engineering, physics, mathematics, data science or closely related quantitative disciplines. Strong Python and machine-learning experience is highly valued. Previous work with neuroimaging, electrophysiological signals, graph-based methods, deep learning or modern foundation models would be advantageous. The successful candidate will join an interdisciplinary and international research environment with access to advanced datasets and technologies, as well as collaborations with academic, clinical and industrial partners. Interested candidates should contact us as soon as possible by sending: * an updated CV * a brief letter describing their background and research interests to nicola.toschi(a)uniroma2.eu<mailto:nicola.toschi@uniroma2.eu>, using the subject line: “PhD Expression of Interest – AI 2026” Please note that this preliminary contact does not replace the formal application. Candidates must also apply through the official portal by 7 September 2026 at 12:00 CET. Official call: https://www.unicampus.it/bando/bando-dottorato-di-ricerca-in-intelligenza-a… Application portal: https://pica.cineca.it/unicampus/2026-2027-phd-ai3-it-isessione Host institution: University of Rome “Tor Vergata”, Rome, Italy Duration: three years Application deadline: 7 September 2026, 12:00 CET Remote interview: 23 September 2026 Please feel free to circulate this announcement among potentially interested candidates. Best regards, Nicola Toschi Prof. Nicola Toschi Scholar Profile<https://scholar.google.com/citations?hl=en&user=kyI6AJ8AAAAJ&view_op=list_w…> | Lab Website<https://fismed.uniroma2.it/> | Bookable Time<https://calendar.app.google/vXmx4XiWRvkf23XQ7> | LinkedIn<https://www.linkedin.com/in/nicola-toschi-5680923/> Full Professor of Medical Physics Medical Physics Section - Department of Biomedicine and Prevention University of Rome "Tor Vergata" Via Montpellier 1 - 00133 Rome (IT) Email: toschi(a)med.uniroma2.it<mailto:toschi@med.uniroma2.it> Research Staff, Investigator A.A. Martinos Center for Biomedical Imaging - Harvard Medical School/MGH 149 13th street, 02129 Boston (MA), USA Email: nicola(a)nmr.mgh.harvard.edu<mailto:nicola@nmr.mgh.harvard.edu>

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Academic positions in Artificial Intelligence at the University of Surrey
by roman bauer Aug. 27, 2026

Aug. 27, 2026

Dear all, We are pleased to announce three open academic positions in Artificial Intelligence at the University of Surrey, UK: Professor in Artificial Intelligence (NICE Research Group within the Computer Science Research Centre) https://jobs.surrey.ac.uk/Vacancy.aspx?ref=037526 Senior Lecturer in Artificial Intelligence (NICE Research Group within the Computer Science Research Centre) https://jobs.surrey.ac.uk/Vacancy.aspx?ref=037426 Professor in Artificial Intelligence (Centre for Vision, Speech and Signal Processing) https://jobs.surrey.ac.uk/Vacancy.aspx?ref=037626 Closing date: Sunday 13 September 2026 The School of Computer Science and Electronic Engineering is seeking to recruit internationally recognised researchers to strengthen its research and teaching in Artificial Intelligence. These appointments form part of a wider recruitment campaign and strategic investment across the Faculty: https://www.jobs.ac.uk/enhanced/linking/university-of-surrey-engineering-ph… The Professor and Senior Lecturer positions in Computer Science cover areas including Large Machine Learning Models, Neurosymbolic AI, Trustworthy Machine Learning, Natural Language Processing, and applications of AI and machine learning in automated reasoning, security, and software and systems development. Both posts are aligned with the Nature Inspired Computing and Engineering (NICE) Research Group within the Computer Science Research Centre. The Professor position within the Centre for Vision, Speech and Signal Processing focuses on leading growth in frontier AI, including multimodal foundation models, machine learning, agentic, people-centred and sustainable AI, with applications spanning human and animal health, biosciences, creative industries, sustainability, robotics and autonomous systems. The School is home to two established research centres with substantial expertise in AI and machine learning: the Computer Science Research Centre and the Centre for Vision, Speech and Signal Processing. Surrey has an established international reputation in AI research, ranking first in the UK for computer vision and among the top five for AI, computer vision, machine learning, robotics and natural language processing according to CSRankings.org. The School was also ranked seventh in the UK for Computer Science research outputs in REF2021. Computer Science and CVSSP are central to the Surrey Institute for People-Centred AI, a pan-University initiative bringing together AI research and expertise across health, engineering, social and behavioural sciences, business, law and the creative arts. The Institute leads a portfolio of more than £100 million in grant awards, including major activities in healthcare and the creative industries, and two doctoral training programmes supporting more than 100 PhD researchers. Informal enquiries concerning the Computer Science positions may be directed to Professor Brijesh Dongol at b.dongol(a)surrey.ac.uk. Enquiries concerning the CVSSP position may be directed to Professor Adrian Hilton at a.hilton(a)surrey.ac.uk. Our staff and students come from around the world, and we are proud of our friendly and inclusive culture. The University is committed to building a diverse community, and applications from under-represented groups are particularly encouraged. Please share these opportunities with potentially interested candidates and across your networks. Best regards, Roman *Roman Bauer, Ph.D.* Senior Lecturer | Head of the NICE Research Group BioDynaMo Spokesperson (www.biodynamo.org) *A software suite to enable simulations of large, complex systems for research or policy making* *Computer Science Research Centre* University of Surrey | Guildford, UK r.bauer(a)surrey.ac.uk | ORCID <https://orcid.org/0000-0002-7268-9359> | LinkedIn <https://www.linkedin.com/in/roman-bauer-05176094/> * Watch the **BioDynaMo introduction video* <https://youtu.be/N6Kzc_AadyI?si=zHymOvrlR3wzAbCf>

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Postdoc Positions in Cognitive and Computational Neuroscience at Washington University in St. Louis
by Runnan Cao Aug. 26, 2026

Aug. 26, 2026

The Multimodal Cognitive Neuroscience Laboratory, led by Dr. Runnan Cao at Washington University School of Medicine in St. Louis, invites applications for postdoctoral research associate positions. Our laboratory investigates the neural computations and network-level dynamics underlying visual perception and social cognition in neurotypical individuals and people with neurodevelopmental or psychiatric conditions, including autism and schizophrenia. We combine human intracranial electrophysiology, including iEEG and single-neuron recordings, with high-resolution fMRI, behavioral experiments, and computational modeling to determine how the brain transforms visual input into representations that support social perception and behavior. These positions offer a distinctive opportunity to join a new laboratory at a formative stage. The successful candidates will help shape its scientific direction, analytical infrastructure, collaborative culture, and multimodal research program. Research Projects will be tailored to each postdoctoral researcher’s expertise, interests, and career goals. Potential research areas include: Neural coding of faces and socially relevant visual information Neurocomputational mechanisms underlying visual perception and social cognition Neural coding and network-level dynamics associated with autism and schizophrenia Multimodal integration of iEEG, single-neuron recordings, high-resolution fMRI, and behavioral data Computational models linking neural activity to perception and behavior AI-enabled methods for analyzing complex multimodal neuroscience data Required Qualifications Applicants must have: A PhD or equivalent doctoral degree in neuroscience, cognitive science, psychology, biomedical engineering, computer science, or a related discipline by the appointment start date A strong research background in fMRI and/or electrophysiology Experience analyzing neuroimaging, neural, behavioral, or computational data Proficiency in scientific programming, particularly Python and/or MATLAB Evidence of research productivity appropriate to career stage Strong written and oral communication skills The ability to work both independently and collaboratively in an interdisciplinary environment Preferred Qualifications Experience in one or more of the following areas is desirable: Acquisition or analysis of 7T fMRI data Human intracranial EEG, ECoG, depth-electrode, or single-neuron recordings Advanced statistical methods, neural signal processing, or network neuroscience Machine learning or deep learning applied to neural and behavioral data Multimodal integration of neuroimaging, electrophysiological, and behavioral data Agentic AI or AI-assisted scientific workflows Development of reproducible data-processing pipelines, workflow systems, or reusable computational tools Candidates who have deep expertise in one methodological area and are eager to develop complementary skills are strongly encouraged to apply. Mentoring and Professional Development Each postdoctoral researcher will work with Dr. Cao to develop an individualized research and career-development plan. Training may include advanced fMRI and electrophysiological analysis, computational modeling, machine learning, scientific writing, grant preparation, project leadership, mentoring, and the development of reproducible research tools. Postdoctoral researchers will be encouraged to establish a clear area of scientific ownership, pursue external funding, build an independent scholarly profile, and develop the skills needed for their chosen career path. Why Join Our Laboratory? Postdoctoral researchers will have the opportunity to: Work with distinctive human intracranial and single-neuron datasets Develop expertise across complementary spatial and temporal scales of brain measurement Help establish the methods, workflows, and scientific culture of a growing laboratory Receive individualized mentoring in research strategy, scientific writing, grant development, and career planning Build collaborations spanning cognitive, computational, clinical, and systems neuroscience Pursue independent fellowships and develop a path toward scientific independence Present their work at national and international scientific meetings The laboratory is committed to rigorous and open science, intellectual curiosity, constructive collaboration, and an environment in which researchers with different backgrounds and methodological strengths can thrive. Why WashU and the Neuroimaging Labs? The laboratory is based in the Neuroimaging Labs Research Center <https://www.mir.wustl.edu/research/research-centers/neuroimaging-labs-resea…> at WashU Medicine’s Mallinckrodt Institute of Radiology. The NIL-RC brings together investigators, postdoctoral researchers, students, clinicians, engineers, and research staff from Radiology, Neurology, Psychiatry, and other departments. The center has a longstanding record of innovation in neuroimaging methods and has supported major scientific initiatives, including the Human Connectome Project and the Alzheimer’s Disease Research Center. Its research spans human cognition, functional connectivity, precision neuroimaging, brain development and aging, psychiatric and neurological disorders, brain parcellation, and imaging methodology. The WashU neuroimaging environment includes: A dedicated research MRI facility <https://www.mir.wustl.edu/research/core-resources/mri-facility/> with three Siemens PRISMA 3T scanners and experienced research imaging personnel A Siemens MAGNETOM Terra.X 7T MRI system <https://www.mir.wustl.edu/magazine/magnified-medicine-7t-comes-to-mir/> supporting ultra-high-resolution structural and functional imaging Internationally recognized expertise in functional connectivity, precision neuroimaging, brain mapping, neuroinformatics, and multimodal imaging Strong connections with Psychiatry, Neurology, Neurosurgery, Neuroscience, Psychological & Brain Sciences, Biomedical Engineering, and Computer Science Opportunities for clinical and translational collaboration in autism, schizophrenia, epilepsy, Alzheimer’s disease, and other neurological and psychiatric conditions A broad seminar, training, and professional-development community across WashU Medicine and the university This environment is especially well suited to researchers who want to connect fine-grained neural recordings with whole-brain imaging, behavior, and computational theory. How to Apply Applicants should email the following materials to Dr. Runnan Cao at r.cao(a)wustl.edu <mailto:r.cao@wustl.edu>: A cover letter describing the applicant’s research experience, methodological expertise, scientific interests, career goals, and potential fit with the laboratory A current curriculum vitae Contact information for two professional references; letters are not required with the initial application Please use the subject line: Postdoctoral Application – [Applicant Name].

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Call for Papers: Novel Insights and Approaches in Time-frequency Analysis
by alireza valizadeh Aug. 26, 2026

Aug. 26, 2026

Dear colleagues, I am writing to share a Call for Papers for a new *Nature Collection in Communications Psychology*, *“Novel Insights and Approaches in Time-frequency Analysis,”* which is now open for submissions: https://www.nature.com/collections/ejbgajabei The Collection brings together new methodological and empirical advances in time-frequency analysis, with broad relevance to neuroscience and related fields. We would particularly welcome work introducing novel approaches, methods, or insights into the analysis and interpretation of time-varying neural signals. The Collection is co-edited by *Gabriele Gratton (University of Illinois Urbana-Champaign)* and *Alireza Valizadeh (IASBS)*. If you have research that may be a good fit, we would be very happy to consider your submission. Please see the Collection page for further details on the scope and submission process. Best wishes, Alireza Valizadeh and Gabriele Gratton ------------------------------------------------------Alireza Valizadeh*Professor * Theoretical Neuroscience lab, Institute for Advanced Studies in Basic Sciences (IASBS), Iran *Principal Investigator* Zapata-Briceño Institute of Neuroscience and Human Intelligence, Spain Tel: (+34) 603325584 valizadeh(a)gmail.com valizadeh(a)institutobz.org <valizadeh(a)humanismoyciencis.org> Web: https://www.institutobz.org/ <https://fundacionhumanismoyciencia.org/instituto-zapata-briceno/> http://www.iasbs.ac.ir/~valizade/index.html --------------------------------------------------------

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2 PhD positions for 4 years with focus on "Generative Episodic Memory" with Prof. Laurenz Wiskott, Germany, Bochum
by Laurenz Wiskott Aug. 25, 2026

Aug. 25, 2026

From https://jobs.ruhr-uni-bochum.de/jobposting/854eabcb1786fa8b53af20c255cd9aea… Faculty of Computer Science : Chair: Theory of Neural Systems In order to fill a fixed-term position in part-time (29.87 hours/week = 75%) at the earliest possible date, we are looking for 2 2 Research associates (m/f/x) for 4 years with focus on "Generative Episodic Memory" The Institute for Neural Computation is a central research institute at the Ruhr-University Bochum, see https://www.ini.rub.de/. It focuses on the dynamics and learning of perception and behavior on a functional level but is otherwise very diverse, ranging from neurophysiology and psychophysics over computational neuroscience to machine learning and technical applications. The research focuses on (i) modeling generative episodic memory in close collaboration with experimental partners, with the goal of modeling experimental data and gaining a deeper understanding of memory processes; (ii) developing a hierarchical model of the visual system that supports a generative memory process. The first project will focus primarily on standard machine learning methods, such as Transformers. The second project aims to develop new methods inspired by the brain. Scope: part-time (75%) Duration: fixed-term, 4 Jahre Start: at the earliest possible date Apply by: 2026-09-14 Your tasks: (i) Further development of an existing system model of generative episodic memory in light of various experimental results. (ii) Development of a brain-inspired hierarchical model of the visual system that supports distributed generative memory processes. Modeling of experimental data. Communication with experimental partners. Publication of results. Teaching assistance equivalent to 3 SWS, in particular conducting a Python course and supervising student/Bachelor/Master projects. Your profile: Requirements: Very good Master degree in mathematics, computer science, engineering, or a related field. Interest in interdisciplinary research in the area of neuroscience. Good programming and mathematical skills. Basic knowledge in machine learning. Advantageous: Experience in modeling. Experience in interdisciplinary projects. Programming skills in Python. We offer: Challenging and varied tasks with a high level of independence Collaboration in a committed and appreciative team Extensive training and professional development opportunities An interesting interdisciplinary environment in the field of theoretical brain research / machine learning. Freedom to shape the project yourself. Infrastructure that facilitates close integration into the institute and the research group. Opportunity to pursue a Ph.D. Further information: The position is salaried and based on the collective agreement of the Länder (TV-L). If the personal and collective agreement requirements are met, the employee will receive pay grade E 13 TV-L. Further information can be found at https://oeffentlicher-dienst.info/ (in German). The place of work is Ruhr University Bochum. The load of teaching will be calculated according to § 3 of Lehrverpflichtungsverordnung (state of North Rhine-Westphalia). Applications (CV, transcript of records for MSc and Bsc, letter of motivation) should be sent as a single pdf file. RUB sees itself as a university with an international presence. The campus languages are German and English. Competence in at least one of the two languages and the willingness to learn the other are a prerequisite. RUB provides corresponding free courses for employees. German language courses are offered by the University Language Center (ZFA) in the field of German as a Foreign Language (DaF). https://www.daf.ruhr-uni-bochum.de/daf/mitarbeitende/index.html.en The Staff Council has the right to participate in all selection interviews. At the request of a candidate (m/f/x), it will ensure its participation in the entire procedure. Please contact wpr(a)rub.de. The Ruhr-Universität Bochum is one of Germany’s leading research universities, addressing the whole range of academic disciplines. A highly dynamic setting enables researchers and students to work across the traditional boundaries of academic subjects and faculties. To create knowledge networks within and beyond the university is Ruhr-Universität Bochum’s declared aim. The Ruhr-Universität Bochum stands for diversity and equal opportunities. For this reason, we favour a working environment composed of heterogeneous teams, and seek to promote the careers of individuals who are underrepresented in our respective professional areas. The Ruhr-Universität Bochum expressly requests job applications from women. In areas in which they are underrepresented they will be given preference in the case of equivalent qualifications with male candidates. Applications from individuals with disabilities are most welcome. Contact persons for further information: Prof. Dr. Laurenz Wiskott Tel.: +49 234 32 27997 Kathleen Schmidt Tel.: +49 234 32 27051 Travel costs, accommodation costs and loss of earnings or other application costs for job interviews can unfortunately not be reimbursed. We look forward to receiving your application via our online application portal at https://jobs.ruhr-uni-bochum.de/en/jobposting/854eabcb1786fa8b53af20c255cd9… by 2026-09-14. Please make sure to mention the reference number ANR 6046.

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Spectral analysis in neuroscience course for non-specialists
by Axel Hutt Aug. 25, 2026

Aug. 25, 2026

============================================================= Course: Spectral analysis of time series in neuroscience: Fundamental methods ============================================================= The 1-day course addresses fundamental aspects of spectral analysis without diving much into theory, just from a practical point of view. The aim is to provide a fundamental understanding of the different techniques out there to avoid mistakes, artefacts and to be able to choose the best technique for the own data. The course is planned like an interactive workshop, where we discuss analysis techniques and clarify open questions and possibly fill gaps of methodology understanding. The course content addresses non-specialists in mathematical analysis, e.g. researchers with education in neurobiology, medicine or psychology, but of course it is also open to theory-oriented researchers who intend to understand the practical side of spectral analysis in neuroscience. Possible topics to be discussed are, e.g. * Sampling theory * Fourier analysis and its possible artefacts, e.g. aliasing, spectral leakage * Time-frequency analysis (Short-Time Fourier Transform, Wavelet Transform, Hilbert Transform and Empirical Mode Decomposition) and its limits in applications * Synchronisation, coherence and Phase-Amplitude Coupling Event information: * Location: Bernstein Center Freiburg im Breisgau, Großer Hörsaal, Hansastr. 9a , 79104 Freiburg, Germany ( [ https://www.bcf.uni-freiburg.de/ | https://www.bcf.uni-freiburg.de ] ) * Date of course and time: Friday, November 27 2026, 9:15-15:45 * Registration : send an email to Axel Hutt (email address: first name.lastname at inria.fr) * Registration deadline: Monday, November 9 2026 * Registration fee: none, free of charge The course is supported by the Neurowissenschaftliche Gesellschaft ( [ https://www.nwg-info.de/ | https://www.nwg-info.de/ ] ). -- Dr. rer. nat. Axel Hutt Directeur de Recherche Equipe NECTARINE - INRIA Nancy Center at University of Lorraine Equipe MLMS - iCube Strasbourg Bâtiment NextMed , 2, rue Marie Hamm 67000 Strasbourg, France Team webpage: https://www.inria.fr/fr/nectarine

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