- Comp-neuro - lists.cnsorg.org

Open PhD and postdoc positions in computational neuroscience/neuroAI
by Roxana Zeraati Sept. 26, 2026

Sept. 26, 2026

Dear all, We are inviting applications for the following collaborative PhD and postdoc positions in computational neuroscience/neuroAI: - PhD (based at TU Dresden, Germany): https://nextcloud.tuebingen.mpg.de/index.php/s/wbpcYQ5pgMjDJos - Postdoc (based at the Max Planck Institute for Biological Cybernetics, Germany): https://nextcloud.tuebingen.mpg.de/index.php/s/wjKjAHRidF5R4M8 We, the Natural Decision Making Group and the Embodied Decision Making Group, are joining forces within the CMC Unit <http://cmclab.org/> to uncover the computational, biological, and ecological principles underlying adaptive decision-making. We combine computational methods from reinforcement learning, dynamical systems, and AI with analyses of neural and behavioral data to investigate how different species adapt their decisions to the dynamics of their natural environments, while accommodating their embodied constraints. We would appreciate it if you could share these calls with anyone who might be interested in your network. Best wishes, Roxana and Shervin

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postdoc or staff scientist
by Canavier, Carmen C. Sept. 25, 2026

Sept. 25, 2026

Carmen Canavier at LSU Health Sciences Center in New Orleans is recruiting a computational neuroscientist(s) at the postdoctoral or staff scientist level. One project involves modeling the local circuit in the medial entorhinal cortex with respect to theta nested gamma and grid cell activity and the other involves modeling the local circuit in hippocampal area CA1 with respect to cholinergic modulation and place field formation. Please email me at ccanav(a)lsuhsc.edu<mailto:ccanav@lsuhsc.edu> for details.

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Postdoctoral position: Computational neuroscience, nonlinear dynamics, and control (University of Arizona)
by drew＠arizona.edu Sept. 24, 2026

Sept. 24, 2026

The Maurer laboratory in the Department of Neuroscience at the University of Arizona is recruiting a postdoctoral researcher interested in theoretical and computational approaches to neural dynamics. A central question is how inhibitory circuitry reshapes the states and transitions available to a recurrent neural network: not simply by reducing excitation, but by dynamically restricting, routing, and stabilizing trajectories through neural state space. We are particularly interested in whether relatively low-dimensional inhibitory or feedback signals can reorganize high-dimensional population activity, alter attractor structure and metastability, regulate transitions among network states, and stabilize circuits that would otherwise be unstable or chaotic. The position sits deliberately between theory and experiment. The researcher will have access to large-scale electrophysiological datasets including neuronal spiking, LFP, EEG, behavior, and circuit perturbations. The goal is to develop mechanistic models that generate discriminating predictions and can be tested against experimental interventions, rather than simply fitting models to neural data. We welcome applicants from computational neuroscience as well as control theory, nonlinear dynamical systems, applied mathematics, engineering, physics, computer science, and related quantitative fields. Prior neuroscience experience is not required. The position is full-time at the University of Arizona in Tucson, with salary according to NIH guidelines and full benefits. Applications are open until the position is filled. University of Arizona posting: req27102 Application materials: CV, cover letter, and statement of research interest. For informal questions, please contact: Andrew P. Maurer, PhD Associate Professor, Neuroscience University of Arizona drew(a)arizona.edu

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Upcoming Open Lab Online seminars and workshops
by Nada El Mahmoudi Sept. 24, 2026

Sept. 24, 2026

Dear all, We are happy to share several upcoming Open Lab Online events on open tools, training, and methods in neuroscience and behavior. Open Lab Online is an international initiative that organizes accessible online seminars and workshops around open-source tools, experimental methods, and training across neuroscience and behavior. Animal Training Methods Across Species From Experimental Design to Naturalistic Behavior This seminar series will explore how animals are trained to interact with experimental tasks, touchscreens, vehicles, virtual environments, and naturalistic paradigms. First confirmed speakers include Kelly Lambert, Jennifer Taylor-O’Connor, Rainer Friedrich, Matthew Isaacson, and Marieke Schölvinck & Martha Nari Havenith, with additional seminars to be announced. Dates: 14 October, 4 November, 25 November, 20 January, 10 February Time: 16:00 CEST / 10:00 EDT for 14 October; 16:00 CET / 10:00 EST from November Full program and registration: https://nenckiopenlab.org/animal-training-methods-across-species/ Neural Data Analysis with Open-Source Tools From Raw Signals to Behavior-Linked Activity This hands-on workshop series will introduce an open-source workflow for neural data analysis, taught directly by the developers of the tools. The series will cover: SpikeInterface → Bombcell → Pynapple → NeMoS Part 1: Spike Sorting and Curation 19–20 October, 15:00–18:00 CEST / 09:00–12:00 EDT each day Modules: SpikeInterface + Bombcell Part 2: Behavior-Linked Analysis and Modeling 26–27 October, 14:00–15:30 CET / 09:00–10:30 EDT each day Modules: Pynapple + NeMoS Full program and registration: https://nenckiopenlab.org/neuraldata/ Bonsai Module I Workshops We will also host two Bonsai Module I workshops for participants interested in behavioral experiments, video tracking, closed-loop systems, and real-time data acquisition. 29 September, 16:00 CEST / 10:00 EDT https://nenckiopenlab.org/event/2200/ 13 October, 16:00 CEST / 10:00 EDT https://nenckiopenlab.org/event/bonsai-module-i-2/ Please feel free to share these events with anyone who may be interested. To receive future Open Lab Online announcements, you can subscribe to the mailing list here<https://5584ba20.sibforms.com/serve/MUIFADA4d4FHqdFYLj0P-heCkOQ5zMtIUiggjmg…>. You can also join the Open Lab Online Discord here<https://discord.gg/z7gvyj9yrV>. Best wishes, The Open Lab Online Team Dr. Nada El Mahmoudi Post-doctoral researcher, Peyrache lab The Neuro (Montreal Neurological Institute) McGill University, Canada

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Announcement: daftar 1.0 — recording what produced each computational result
by Aslan Satary Dizaji Sept. 24, 2026

Sept. 24, 2026

Dear all, I would like to share an open-source tool that may be useful to people running simulations and model fits. The problem. Experiment trackers such as MLflow and Weights & Biases assume an experiment is a training loop with epochs, losses and checkpoints. A Hodgkin–Huxley simulation, a hierarchical fit across participants, or an EEG preprocessing pipeline has none of those. Months later it is often impossible to say which code version, environment and seed produced a given figure. What it does. One decorator or context manager around existing code records the environment, git state, every parameter, every seed, and content hashes of inputs into a plain-JSON manifest. daftar diff then compares two runs and separates fields that could have caused a difference from fields that merely record one — including the case where results differ and nothing that could have caused it does, which is a finding rather than a glitch. Seeds are applied rather than merely recorded, since noting that a run was irreproducible does not help anyone. Ten adapters record framework-specific state a generic tracker cannot infer: the integration method Brian2 auto-selected (it is stored nowhere), which ICA components were excluded in MNE, which confounds were regressed out in Nilearn, whether a NEURON compiled mechanism is stale relative to its .mod sources. No dependencies, no server, no account, no network access. Apache 2.0. Currently in peer review at pyOpenSci. pip install daftar https://github.com/aslansd/daftar One thing that emerged from building it, which may interest this list: four independent frameworks (MNE, cpm, Brian2, sbi) all report only one of the two reasons an optimiser stops. A fit that hit its iteration limit is indistinguishable from one that converged. I would welcome criticism, particularly from anyone who has previously tried a reproducibility tool and stopped using it. Aslan Satary Dizaji https://turan-cybernetics.com/ https://sites.google.com/a/umich.edu/aslansdizaji

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30 RTAIM Online Seminar - "Assessing the Ethical Risks of AI-Based Tools for Causal Inference from Real-World Evidence (RWE) in Medicines Evaluation"
by steven gouveia Sept. 23, 2026

Sept. 23, 2026

[??] 30 RTAIM Online Seminar [??] 24 September | 14:00 (Porto) [??] Online via Microsoft Teams Tamas Soroncz-Szabo Physician, epidemiologist, visiting lecturer in clinical research methods, and medical project expert for the European Health and Digital Executive Agency (HaDEA). [???] Assessing the Ethical Risks of AI-Based Tools for Causal Inference from Real-World Evidence (RWE) in Medicines Evaluation The talk will examine how AI is increasingly used to analyse real-world health data for medicines evaluation — and the ethical and regulatory risks that arise when AI-supported conclusions can influence efficacy, patient safety, and healthcare decisions. [??] Join: https://teams.microsoft.com/meet/389914877372834...<https://teams.microsoft.com/meet/389914877372834?p=2VyrR9YvPfgVE9glG6&fbcli…> Meeting ID: 389 914 877 372 834 Passcode: Vn95Mb6h More info: https://ifilosofia.up.pt/activities/seminar-30-rtaim

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New PhD & Postdoc Opportunities in Computational Neuroscience & Computational Psychiatry
by Nadja R. Ging Jehli Sept. 23, 2026

Sept. 23, 2026

Dear colleagues, We are building a new computational modeling hub at the University of Arizona - join us and grow with us! Ari Kahn and I are joining forces to build a closely connected computational community across two complementary labs at the intersection of decision science, cognitive neuroscience, and computational psychiatry. Together, the Kahn Lab and the NAIADEL Lab are recruiting multiple PhD students for Fall 2027 as well as postdoctoral researchers. Our groups tackle some shared questions from complementary angles: The Kahn Lab studies how people build predictive models of complex environments, use them to plan, and select among planning strategies. This work draws on reinforcement learning, successor representations, network science, control theory, and neuroimaging. Learn more here: www.kahnlab.org<https://www.linkedin.com/safety/go/?url=http%3A%2F%2Fwww%2Ekahnlab%2Eorg&ur…> The NAIADEL Lab studies how humans and artificial agents infer whether their actions matter, adapt when control shifts, and decide whether to persist, switch, or disengage after setbacks. This work draws on reinforcement learning, sequential sampling models, neural networks, and economic theory. Learn more here: www.naiadel.com<https://www.linkedin.com/safety/go/?url=http%3A%2F%2Fwww%2Enaiadel%2Ecom&ur…> Where we converge: We are especially excited about the intersection of meta-learning and meta-control. How do adaptive agents, biological or artificial, select among strategies? How do they recognize when a strategy is failing? And how do they revise their internal models under uncertainty and partial control? Trainees will be based primarily in one lab while benefiting from joint research meetings, shared computational infrastructure, and opportunities for cross-lab collaboration and co-mentorship. How to get involved: Prospective PhD students: Drop us a line with your CV and research interests. The Fall 2027 Psychology PhD application window opens October 15: Ph.D. Cognition and Neural Systems | Psychology<https://psychology.arizona.edu/cns> Postdoctoral candidates and other inquiries: Reach out directly with your CV and a brief statement of your research interests. arikahn(a)arizona.edu<mailto:arikahn@arizona.edu> nadjaging(a)arizona.edu<mailto:nadjaging@arizona.edu> We are very grateful for anyone who shares this within their broader network. Best, Nadja Nadja R. Ging-Jehli, PhD Assistant Professor Neurocomputation, Adaptive Intelligence, Agency Dynamics, & Embodied Learning (NAIADEL Lab) | www.naiadel.com<http://www.naiadel.com/> Cognition and Neuroscience Systems, Department of Psychology University of Arizona (614) 736-7755 | nadjaging(a)arizona.edu<mailto:nadjaging@arizona.edu> | nadja(a)gingjehli.com<mailto:nadja@gingjehli.com> | gingjehli.com<https://www.gingjehli.com/> [A button with "Hear my name" text for name playback in email signature]<https://www.name-coach.com/nadja-gingjehli>

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Call for Papers: ACML 2026 (Encore Track)
by Levin Kuhlmann Sept. 22, 2026

Sept. 22, 2026

ACML 2026 (the 18th Asian Conference on Machine Learning) is being held in Melbourne Australia on 1–4 December, just before NeurIPS in Sydney. The main conference, journal and workshop tracks have closed, but the Encore Track is still accepting submissions. It's a way to present work you've already had published or accepted at a leading venue (including NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, AAAI, JMLR, IEEE TPAMI) since 1 June 2025, through an encore talk in a topic-aligned session. Submission is lightweight: title, abstract, author details, PDF and publication URL (or evidence of acceptance if not yet published). Deadline: 18 October 2026, 11:59 PM AoE For more details visit https://www.acml-conf.org/2026/calls/papers/#encore-track If you have any questions, please contact Levin Kuhlmann ( levin.kuhlmann(a)monash.edu) Kind regards, Levin Kuhlmann Encore Track Chair, ACML 2026

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NeuroData AI Summer School: applications open for July 2027 at HHMI-Janelia
by Ben Dichter Sept. 22, 2026

Sept. 22, 2026

Applications are open for the NeuroData AI Summer School, a one-week residential program on reanalyzing open neurophysiology data, July 19 to 24, 2027 at HHMI's Janelia Research Campus in Ashburn, Virginia (arrive July 18, depart July 24 or 25). The school continues NeuroDataReHack, which ran five times from 2022 to 2026, and is now supported by a five-year NINDS R25 award. Participants learn to find, stream, and analyze NWB datasets on the DANDI Archive with tools such as SpikeInterface, Pynapple, NeMoS and Neurosift, and spend most of the week in small teams on a reanalysis project of their own. New this year is a track on AI methods for neural data, from foundation models to working effectively and responsibly with coding agents. Lectures are given by the people who build the tools, and every lecture is recorded and published. The school is free to attend. Lodging and meals are covered, and funding for travel is available. We welcome PhD students, postdocs, and faculty from all areas of neuroscience with basic Python or MATLAB experience. Preference goes to applicants with a specific reanalysis project in mind. Applications close January 29, 2027 and are reviewed on a rolling basis: https://neurodata-ai.org/apply <https://neurodata-ai.org/apply/?utm_source=SOURCE&utm_medium=email&utm_camp…> Past projects, recorded lectures, and reports from previous years: https://neurodata-ai.org <https://neurodata-ai.org/?utm_source=SOURCE&utm_medium=email&utm_campaign=a…> Questions: ben.dichter(a)catalystneuro.com Ben Dichter Program Director, NeuroData AI Summer School

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Call for applications: Gatsby Unit PhD Programme 2027 entry
by I-Chun Lin Sept. 22, 2026

Sept. 22, 2026

4-Year PhD Programme in Theoretical Neuroscience and Machine Learning Deadline: 17:00 GMT, Monday 9 November 2026 Applications are now open for the 2027 intake of the Gatsby Unit PhD programme at University College London (UCL). Students study toward a PhD in either machine learning or theoretical neuroscience, with minor emphasis in the complementary field. New students will join at the start of the academic year in September. First-year courses provide a solid foundation in machine learning, theoretical neuroscience and systems neuroscience, after which students pursue PhD projects specialising in machine learning, theoretical neuroscience, or both. Students benefit from close interaction with researchers at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour and the ELLIS Unit at UCL, alongside strong links with other research groups across UCL and beyond. Full funding is available regardless of nationality. The studentship covers UCL tuition fees for both home and international students and includes an annual tax-free stipend as well as travel budget for attending conferences and workshops. We also welcome applicants who have secured, or are seeking, funding from other sources. The applicant should have a strong analytical and mathematical background, a keen interest in neuroscience, machine learning or both, and a relevant first degree, for example in Mathematics, Statistics, Computer Science, Engineering, Physics, Neuroscience or Cognitive Psychology. Applications should be submitted directly to us through the Gatsby Recruitment Portal. Please review the information on the programme webpage (www.ucl.ac.uk/life-sciences/gatsby/study-and-work/gatsby-unit-phd-programme<http://www.ucl.ac.uk/life-sciences/gatsby/study-and-work/gatsby-unit-phd-pr…>) carefully before starting your application. About the Unit The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. Our research focuses on the mathematical principles of learning, perception, cognition and action in brains and machines. We study causality; neural systems; neural representations; neural data analysis; computation and dynamics; learning; theory of deep learning; reinforcement learning; kernel methods; graphical models; Bayesian statistics; network and relational data. For more information on our research, visit www.ucl.ac.uk/life-sciences/gatsby/research<https://www.ucl.ac.uk/life-sciences/gatsby/research>. We are a community of around 50 academic, research and support staff, and students, committed to building an inclusive and supportive research environment. We function as far as possible as a single research group, with strong cohesion fostered via unit-wide activities such as tea hours, external seminars and journal clubs. Many leading researchers in machine learning and theoretical neuroscience have studied or worked in the Unit, including two Nobel Prize winners. Over 40% of our PhD and postdoctoral alumni hold faculty positions, while around 30% work in research development roles at companies such as Google DeepMind (itself founded by two of our alumni), Meta and Anthropic. Find out more about our faculty members and network of alumni at www.ucl.ac.uk/life-sciences/gatsby/people<https://www.ucl.ac.uk/life-sciences/gatsby/people>. -- I-Chun Lin Scientific Programme Manager Gatsby Computational Neuroscience Unit, UCL

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