October 2026 - Comp-neuro - lists.cnsorg.org

PhD in Perceptual Learning (fMRI + modelling) in San Sebastian, Spain
by Alejandro Tabas Oct. 1, 2026

Oct. 1, 2026

Dear friends and colleagues, The Perceptual Inference Group at the BCBL (Basque Center on Cognition, Brain and Language) in San Sebastian, Spain, is hiring a PhD to work on a project investigating how the brain learns the statistical structure of sensory world across sensory modalities. The project will combine fMRI and computational modelling, with more reliance on one or the other depending of the preferences and background of the student. This is a great opportunity for cognitive neuroscientists who want to learn computational modelling, or for computational neuroscientists who want to learn state of the art human neuroimaging. The group is currently partially embedded in the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig and there may be opportunities for collaborations. The PI, Alejandro Tabas, currently holds an ERC Starting Grant and there's plenty of funding for conferences and equipment. The BCBL is an international, multidisciplinary research centre in San Sebastian, a stunning coastal city renowned for its quality of life and its amazing gastronomy. The centre offers state-of-the-art neuroimaging infrastructure, including in-house MEG and fMRI machines, and strong support for work-life balance and professional development. Some details: 4-year contract, full-time Start date is flexible (January to April 2027) Application deadline: 8th of November 2026 Full details and application link: https://calls.bcbl.eu/index.php?option=com_myprofile&Itemid=278&task=view&i… Alejandro <tabas(a)bcbl.eu> is very happy to hold informal chats about the project and the position with prospective applicants. Please contact BCBL's HR (email in the application link) for questions about the application process, deadline, etc. Apologies for cross-posting. Best, Alejandro

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Postdoc position in model-based neurostimulation in Strasbourg (France)
by camille.gontier＠inria.fr Oct. 1, 2026

Oct. 1, 2026

Dear colleagues, I am recruiting a postdoc to join our newly created research team, jointly hosted by Inria (https://www.inria.fr/en/nectarine) and the University of Strasbourg (France). The aim of our project is to develop efficient closed-loop control methods for neurostimulation and to apply them to help in the treatment in various psychiatric disorders. It is highly interdisciplinary, and sits at the crossroads between machine learning, engineering, and clinical neuroscience. We are thus looking for motivated candidates with a strong quantitative background, experience in implementing ML models, and interest in clinical applications. The position is expected to start in December 2026, although the exact starting date can be flexible. The first contract will have a duration of 14 months, and can be renewed until May 2029. Interested candidates should send a CV and a brief statement of interest (up to 1 page) to camille.gontier(a)inria.fr Best regards, Camille Gontier Tenured faculty, Inria

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UniReps in Paris 🔵🔴 We have a date!
by Organizers Oct. 1, 2026

Oct. 1, 2026

Hi all, We’re excited to announce the UniReps workshop, taking place on December 12th coinciding with NeurIPS in Paris! The Call for Papers <https://unireps.org/2026/call-for-papers> for the 4th edition of UniReps <https://unireps.org/2026/> is live! Submit via OpenReview <https://openreview.net/group?id=UniReps.org/2026/Workshop> Deadline: 4th October 2026 (AoE) Our workshop theme focuses on a central question: Why, when, and how do distinct learning processes yield similar representations, and to what extent can these be unified? Neural systems, whether artificial or biological, often converge on structurally similar internal representations when exposed to semantically related inputs. This workshop brings together researchers from Neuroscience, Artificial Intelligence, and Cognitive Science to build a unified understanding of this phenomenon and explore its theoretical and practical implications. We invite contributions addressing the following questions: - When? Under what conditions do similar representations emerge, and how can we measure this? - Why? What mechanisms, biological or computational, drive representational convergence? - What for? How can we harness this understanding for modular deep learning, model merging, transfer learning, and beyond? *Topics of Interest *Here’s a non-exhaustive list of themes we’re excited to explore, targeting especially their relationship with the questions listed above: - Model merging, stitching, and compositionality - Representational alignment and similarity measures - Identifiability and symmetry in neural models - Learning dynamics and mechanistic interpretability - Multimodal and multiview learning - Disentangled representations - Linear mode connectivity - Representation Similarity Analysis (RSA) - Multi-agent communication - Mechanistic interpretability - Representation universality Submission Tracks Full Paper (Archival) - Up to 9 pages (excluding references and appendix) - Anonymous submission - Published in dedicated workshop proceedings - Poster + spotlight opportunities Extended Abstract (Non-Archival) - Up to 4 pages - Anonymous submission - Poster + spotlight opportunities - Perfect for early-stage work, negative results, or opinion pieces Both tracks are eligible for best papers awards. *Join Our Program Committee *We’re looking for dedicated reviewers to help maintain the high standard of our review process. Each reviewer is assigned no more than two submissions, and we’re planning to introduce Reviewer Awards to recognise outstanding contributions. Interested? Sign up here <https://docs.google.com/forms/d/e/1FAIpQLScGnnXH13fNw50Sm09oeXvTup-ZkpmSLq4…> to join the UniReps 2026 Program Committee. We look forward to your submissions and hope to see you at UniReps! Warm regards, The UniReps Organising Team

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Postdoctoral Position: Autism, Psychosis, Computational Psychiatry & Multimodal Neuroimaging (Stanford University
by daelsaid＠stanford.edu Oct. 1, 2026

Oct. 1, 2026

Dear All, We are seeking a postdoctoral scholar to join Dr. Kaustubh Supekar's laboratory (https://med.stanford.edu/supekar-lab/education.html) and contribute to two NIH R01-funded projects at the intersection of neuroscience, artificial intelligence, and precision psychiatry. The Supekar lab integrates neuroscience and artificial intelligence (AI) to advance understanding of brain function and the mechanisms underlying neuropsychiatric disorders. Through rigorous research and clinically oriented translation, we develop evidence-based innovations to improve diagnosis, prognosis, and treatment, with the ultimate goal of improving outcomes for individuals affected by mental illness. The postdoctoral scholar will work on projects focused on autism and schizophrenia-spectrum disorders, using large-scale neuroimaging, clinical, cognitive, and molecular datasets to discover neurobiological patterns that can inform mechanistic understanding as well as diagnosis, prognosis, and treatment. One project focuses on identifying biologically grounded subtypes of psychiatric disorders, particularly autism, using novel computational approaches, neuroimaging-derived brain circuit fingerprints and transcriptomic signatures. A second project focuses on developing multimodal prognostic markers for psychosis in youth at clinical high risk, using state-of-the-art AI models and multimodal neuroimaging, clinical, and cognitive data. The position will emphasize advanced computational and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications, develop independent lines of inquiry, present at national and international conferences, and collaborate with a multidisciplinary network of investigators. The position provides strong mentorship and career development support for candidates pursuing independent research careers in precision psychiatry, neuroimaging, computational neuroscience, AI for mental health, and/or multimodal data science. Required Application Materials: 1. Brief cover letter describing research interests, relevant experience, research accomplishments, career goals, and fit with the position 2. Current CV 3. Contact information for three references 4. Up to three representative publications or preprints Required Qualifications: - A PhD, MD/PhD, or equivalent research doctoral degree in neuroscience, biomedical data science, computer science, psychology, psychiatry, statistics, engineering, applied mathematics, or a related field - Demonstrated expertise in one or more of the following areas: - Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling - Human neuroimaging analysis, including functional MRI, structural MRI, connectivity, or brain circuit modeling - Computational psychiatry, clinical neuroscience, or developmental psychopathology - Analysis of large-scale multimodal datasets - Explainable AI, predictive modeling, stratification, or biomarker development - Programming in Python, R, MATLAB, or related scientific computing environments - Reproducible research practices, including version control, high-performance computing, and open science workflows - Strong quantitative skills as well as excellent written and oral communication abilities. - A strong record of scholarly productivity - The ability to work both independently and collaboratively within a multidisciplinary research team - Prior experience with autism, psychosis, schizophrenia-spectrum disorders, developmental psychopathology, or neurobiological datasets is desirable but not required Required Application Materials: - Brief cover letter describing research interests, relevant experience, research accomplishments, career goals, and fit with the position - Current CV - Contact information for three references - Up to three representative publications or preprints Please email application materials as a single PDF to supekarlab(a)stanford.edu with the subject line: Postdoctoral Scholar. For additional information about the position, please contact Dr. Supekar at the same email address. Applications will be reviewed on a rolling basis until the position is filled. The anticipated start date is flexible. Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.

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