- Comp-neuro - lists.cnsorg.org

Research Assistantship (pre-doctoral route) position in NeuroAI in Coimbra, Portugal

by Jorge Almeida

Dear colleagues, We are recruiting a Research Assistant with a Master’s degree (this is a requisite) to join the newly funded ALIGN project, supported by the CMU Portugal / FCT Initiative. The position will be based at the University of Coimbra, working with Jorge Almeida and in close collaboration with Michael Tarr at Carnegie Mellon University. The project sits at the intersection of NeuroAI, cognitive neuroscience, computer vision, and human-centered AI, and aims to understand how visual representations in AI models align with human behavioral and neural representations. The selected candidate will contribute to behavioral and neuroimaging research, AI model evaluation, and representational analyses involving vision and vision-language models such as CLIP, DINO, ResNet, and ViT. This is an excellent opportunity for someone interested in developing a research profile in NeuroAI and potentially continuing into a PhD in this area. The position is funded through a Master’s research fellowship, with a monthly stipend of €1,359.64, tax-free (as dictated by the Portuguese Government). The contract will be for the duration of the project – expected 1 year - although we expect the candidate to apply together with the team to a PhD fellowship to continue the project. We are looking for candidates with interests in one or more of the following areas: - Cognitive neuroscience or psychology - Artificial intelligence, machine learning, or computer vision - Neuroimaging, especially fMRI - Data analysis, Python programming, or computational modeling - Human perception, categorization, or visual cognition Interested candidates should send a CV, a brief statement of interest, and contact information for references to: jorgecbalmeida(a)gmail.com Please feel free to share this opportunity with anyone who may be interested. Best, Jorge Almeida University of Coimbra ALIGN Project | CMU Portugal / FCT Initiative

Oct. 21, 2026

on behalf of Greg Reynolds: position in Music, Science and Health at Knoxville

by Reinoud Maex

Begin forwarded message: From: "Reynolds, Greg" <greynol3(a)utk.edu<mailto:greynol3@utk.edu>> Subject: Request to post job ad to listserv Date: 16 October 2026 at 16:54:30 BST To: "comp-neuro-owner(a)lists.cnsorg.org<mailto:comp-neuro-owner@lists.cnsorg.org>" <comp-neuro-owner(a)lists.cnsorg.org<mailto:comp-neuro-owner@lists.cnsorg.org>> You don't often get email from greynol3(a)utk.edu<mailto:greynol3@utk.edu>. Learn why this is important<https://aka.ms/LearnAboutSenderIdentification> Please post to the listserv this job ad for an open position as Assistant/Associate/Full Professor of Music, Science, and Health, Natalie L. Haslam College of Music at the University of Tennessee, Knoxville. The Natalie L. Haslam College of Music invites applications for a 9-month tenure-track position at the Assistant, Associate, or Full Professor level in Music, Science, and Health. This is a rare and forward-looking appointment designed for a scholar-teacher who believes that a fuller understanding of music — how we hear it, perform it, feel it, analyze it, and create it — is enriched by understanding the human brain, human behavior, and the science behind how we interact with the art of music and human health. The successful candidate will occupy a distinctive role at the intersection of music, medicine and health, neuroscience, psychology, cognitive science, or other theoretical and applied sciences, contributing to both the core curriculum of the College and to an emerging cross-disciplinary research culture. The candidate will work in the Natalie L. Haslam College of Music with potential for collaboration with other colleges, and relevant health organizations. The successful candidate is expected to have either existing or strong potential for obtaining extramural funding. More information and application materials can be found here: https://apply.interfolio.com/194546<https://apply.interfolio.com/194546> ______________________________________ Greg D. Reynolds, Ph.D. Professor Department of Psychology & Neuroscience University of Tennessee, Knoxville http://dcnlab.utk.edu<http://dcnlab.utk.edu>

Oct. 20, 2026

Faculty position in Computational Neuroscience at Boston University

by Emily Stephen

The Boston University Department of Mathematics and Statistics is recruiting an Assistant Professor of Computational Neuroscience as part of Boston University’s Advancing Brain and Cognitive Health convergent search—part of a new University-wide approach to convergent research. The convergent search will involve up to 6 tenure-track faculty hires. Going beyond traditional interdisciplinary collaboration, convergent research intentionally brings together different disciplines, methods, and ways of thinking to tackle complex challenges and open new directions for discovery. Faculty joining this cluster will have opportunities to build collaborations across neuroscience, computation, engineering, and health sciences to pursue ambitious questions about the brain and cognitive health. Learn more about the position and the other openings in the convergent search: https://bit.ly/4hP8ya0 Best, Emily Stephen

Oct. 19, 2026

PhD and postdoc positions in computational and theoretical neuroscience || University of Texas at Dallas

by Muller, Lyle

PhD and postdoctoral positions are available in a growing cluster of computational neuroscience labs at the University of Texas at Dallas. The Muller Lab (http://mullerlab.io<http://mullerlab.io/>) and the Lakshminarasimhan Lab (https://neuralalgorithms.github.io<https://neuralalgorithms.github.io/>) are recruiting new trainees in computational and theoretical neuroscience. PhD positions start in Fall 2027. Postdoctoral start dates are flexible. Research across the cluster combines computational analysis of neural data, applied mathematics, and machine learning to understand computation in biological and artificial neural networks in close collaboration with experimental groups. Projects in the Muller Lab include traveling waves in neural systems (link<https://www.nature.com/articles/s41467-023-39076-2>) and analyses of spikes in large neural populations (link<https://arxiv.org/abs/2412.03804>). The group has an active collaboration with the Fields Institute through the Lab for Network Computation<https://www.fields.utoronto.ca/activities/fields-lab-network-computation>. The Lakshminarasimhan Lab develops computational theories of learning, memory and decision-making that link synaptic plasticity, neural dynamics and behavior. Ongoing projects in the lab include developing and testing normative theories of dopamine signaling (link<https://www.biorxiv.org/content/10.1101/2025.03.18.643990v2.full>) and representational drift. The two labs are building shared training in computational neuroscience and NeuroAI at UTD. Trainees will join a growing computational neuroscience community in the Dallas area, including connections to UT Southwestern and the Texas Artificial Intelligence Research Institute (TAIRI). Prior experience with scientific computing, signal analysis, applied mathematics, or machine learning is preferred. Prospective PhD students should apply to the Cognition and Neuroscience PhD program at UTD (link<https://bbs.utdallas.edu/departments/neuroscience/graduate-programs/cogniti…>) before December 1, 2026. Prospective postdocs can write to lyle.muller(a)utdallas.edu or kaushik.laksh(a)utdallas.edu with a CV and short statement of interest. -- Lyle Muller http://mullerlab.io

Oct. 19, 2026

Faculty position in computational neuroscience || University of Texas at Dallas

by Muller, Lyle

The Department of Neuroscience at the University of Texas at Dallas invites applications for a tenure-system faculty position at the Assistant or Associate Professor rank in computational neuroscience, starting Fall 2027. The position is part of a cluster hiring initiative to build an internationally recognized computational neuroscience research and training program at UT Dallas. We are looking for candidates with expertise in neural coding, computation and dynamics, or working at the intersection of neuroscience and artificial intelligence, including large language models. The ideal candidate would do highly collaborative dry-lab research connected to departmental strengths in learning and memory, decision making, motor control, affective neuroscience, sensory perception and pain, or neurogenomics. The position includes teaching undergraduate and graduate courses. Applicants should have a PhD or equivalent in neuroscience, computer science, mathematics or a related field. The new hire will join a rapidly growing computational neuroscience cluster at UTD, with close ties to the Texas Artificial Intelligence Research Institute (TAIRI), the Texas Biomedical Device Center, and UT Southwestern Medical Center. Full position description and application: https://jobs.utdallas.edu/postings/32729. Priority will be given to applications received by December 31, 2026. -- Lyle Muller http://mullerlab.io

Oct. 19, 2026

World wide VVTNS series: Seventh season opening lecture, Wednesday, October 21, 2026, at 11:00 am EDT - Richard Hahnloser, ETH Zurich

by David Hansel

[image: VVTNS.png] https://www.wwtns.online <https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.on…> - on twitter: wwtns@TheoreticalWide You are cordially invited to the seventh season opening lecture of the online VVTNS series. It will be delivered by Richard Hahnloser ETH Zurich on the topic of The Algorithm(s) of Birdsong Learning The lecture will be held on zoom on October 21, 2026, at *11:00 am EDT * To receive the link: https://www.wwtns.online/register-page *Abstract: *How do songbirds acquire and refine their complex vocal repertoires? Learning a song requires both evaluating sensory evidence and determining which changes in behavior improve performance. These challenges have traditionally been studied in separate experimental paradigms: auditory-feedback adaptation, reinforcement learning, and imitation. In this talk, I will examine the behavioral and neural algorithms underlying each and ask how they might fit within a common learning architecture. First, when auditory feedback is shifted in pitch, birds change their vocal pitch in the opposite direction. As in humans, compensation is typically incomplete and becomes proportionally smaller for larger shifts. This pattern is consistent with Bayesian causal inference: birds combine internal predictions of pitch with auditory evidence while discounting feedback that is unlikely to have been self-generated. Second, I will show why classical policy-gradient reinforcement learning falls short in explaining the nonlinear pitch trajectories observed in conditioning experiments. A normative extension accounts for these trajectories and aligns with the modular organization of the brain’s song-control system. In this architecture, a learning component generates exploratory variation and is updated by reward. Other components contribute to motor output but are not updated by reward on every trial. Third, I will turn to imitation learning. Songbirds simplify this computational challenge by acquiring vocal units—syllables—independently of their sequential order. The intrinsic reward guiding the acquisition of this vocal inventory can be characterized by a simple sum–max rule: each target syllable contributes a partial reward determined by its best pitch match among the bird’s developing vocalizations, and these rewards are summed across targets. A light-tailed pitch-similarity function ensures that large mismatches contribute little reward. Together, these findings identify complementary mechanisms for interpreting sensory feedback, assigning credit to motor actions, and matching vocal output to remembered targets. The next challenge is to explain how these mechanisms interact within a unified theory of birdsong learning. I will conclude with initial attempts to use AI to develop this synthesis, with the aim of turning a collection of task-specific algorithms into a coherent framework with testable predictions and potential relevance beyond the avian brain. ᐧ

Oct. 18, 2026

Göttingen Symposium "Living Networks": Nov 30-Dec 2 2026

by Priesemann, Viola

Dear all, Please spread the word: This symposium is interesting to anyone wanting to understand the similarities and difference in network dynamics across sociology, (animal) group behavior and living neural networks. It is free of charge and we have a great international speakers. Best wishes, Viola Dear colleagues, We are pleased to announce "Living Networks" - an international symposium on self-organization, manipulation, and information flow in neural and social systems. We bring together experts on adaptive networks from societal dynamics, animal groups, living neural networks, and complex systems. Please share this announcement with interested colleagues, students, group members, and collaborators. We warmly welcome you to Göttingen - free of charge. Dates: 30 November–2 December 2026 Venue: Manfred-Eigen-Hörsaal, Am Fassberg 11, 37077 Göttingen, Germany 🔗 More information and registration (free of charge): https://priesemann-group.github.io/living-networks/ The symposium poster is available for download here: https://owncloud.gwdg.de/index.php/s/LCp1TDN87tWHRG6 We look forward to welcoming you to Göttingen. Best wishes, From the organization team: Julia Fischer, Greta Jasser, Thomas Kneib, Piklu Mallick, Viola Priesemann Living Networks: Self-Organization, Manipulation and Information Flow in Neural and Social Systems Abstract Living Networks, including societies, groups and also brains, are continuously reshaping their structure, interactions, and information flow among nodes - yet their large-scale structure and function remain largely invariant. Classic examples are scale-free network properties and robust information processing despite variable neural representation. We want to explore whether these networks are shaped by similar self-organization principles, show similar emergence of robustness and resilience, and are potentially also vulnerable tosimilar manipulation mechanisms. By combining the perspective of complex systems with expertise from sociology and neuroscience, our aim is to identify common principles, but also core differences, that drive the emergence of structure, dynamics, and information flow - from neural networks to societal dynamics. Background: From November 30 to December 2nd, 2026, at the University of Göttingen, we bring together international experts across disciplines - including physics, data science, neuroscience, law, and social and political sciences. With invited and contributed talks, ample time for discussions and poster presentations, we will sharpen our understanding of how self-organization mechanisms drive structure and information flow in social networks, as well as neural networks, with implications for understanding disinformation, polarization, and societal resilience. The symposium is organized by the CIDAS Section "Complexity, Information, Cognition, Society" (CICS), funded by the University of Göttingen, and aims to foster future collaborative research initiatives across disciplines. _____________________ Prof. Dr. Viola Priesemann Max Planck Institute for Dynamics and Self-Organization & Georg-August-University Göttingen Am Fassberg 17, 37077 Göttingen viola.priesemann(a)ds.mpg.de<mailto:viola.priesemann@ds.mpg.de> https://priesemann-group.github.io<https://priesemann-group.github.io/>

Oct. 17, 2026

[Invitation] rTAIM Seminar #31 | John Dorsch — Epistemically Trustworthy AI & Collective Accountability | 23 October 2026

by steven gouveia

[??] UPCOMING SEMINAR | rTAIM SEMINAR SERIES #31 [??] Why We Need Epistemically, Not Morally, Trustworthy AI: Decision Support and the Need for Collective Accountability [???] Speaker: John Dorsch (Center for Environmental and Technology Ethics — Prague) [??] Date: 23 October 2026 (Friday) [??] Time: 3:00–4:30 PM (Lisbon Time) [??] Format: Online | Microsoft Teams [??] Free and Open to Everyone! Can artificial intelligence really be trustworthy? And who should be held accountable when AI influences human decisions? The discussion will explore crucial questions concerning AI-assisted decision-making, epistemic trust, institutional accountability, and the implications of the EU AI Act. Join us for the 31st seminar of the Rebuilding Trust in AI Medicine (rTAIM) series, featuring John Dorsch, a researcher specialising in AI Ethics and Philosophy of Cognitive Science. Short bio: John Dorsch is a postdoctoral researcher in AI Ethics and Philosophy of Cognitive Science. Previously, he worked at Ludwig Maximilian University (LMU) Munich in the interdisciplinary institute Munich Interactive Intelligence Initiative (MI3), member of the lab Cognition, Values, and Behavior (CVBE). His research focuses on philosophical and scientific problems surrounding metacognition, as well as AI technology exerting an influence on metacognitive capacities, whether by enhancing or hijacking them. [??] Seminar details and access: https://teams.microsoft.com/meet/348952916094000?p=fPGkR9WtycP2UVboJD ID: 348 952 916 094 000 Password: tp9aU3Ut Everyone interested in the philosophy and ethics of AI, epistemology, technology, and healthcare is warmly invited to attend! Feel free to share this event with colleagues, students, and researchers.

Oct. 16, 2026

Postdoctoral positions in Neuromorphic Computing @ The Hotchkiss Brain Institute

by Wilten Nicola

The Hotchkiss Brain Institute in the Cumming School of Medicine at the University of Calgary is accepting applications for a Postdoctoral Scholar in the Neuromorphic Opportunities, Ventures, & Acceleration (NOVA) program. Job Description: The Hotchkiss Brain Institute invites applications for the HBI Neuromorphic Opportunities, Ventures, & Acceleration (NOVA) program. NOVA sits at the interface of neuroscience, AI, and hardware engineering and supports fellows in translating principles of neural computation into new computing architectures. NOVA aims to attract ambitious early career researchers interested in advancing neuromorphic hardware, algorithms, and software development, through novel developments in brain-inspired technologies to create the next-generation of AI accelerators. The ideal candidates will develop an independent research program in one of the following areas: - Energy-efficient analog and mixed-signal neuromorphic computing - Learning algorithms for neuromorphic systems, including on-chip and biologically inspired learning - Large-scale neural-system emulation using FPGAs and existing neuromorphic platforms such as Intel Loihi and SpiNNaker - Next-generation neuromorphic hardware, including emerging approaches such as neurophotonics Scholars are appointed for an initial term of 2 years, with potential to renew up to a total of 5 years contingent on successful annual review. This award pays an annual stipend of $100,000 CAD and a separate research allowance of $25,000 CAD per year. Successful candidates will be supported by a primary mentor as well as a mentoring committee with diverse experience relevant to the project. Potential mentors, and more information about neuromorphic computing and computational neuroscience at the HBI, can be found here: https://hbi.ucalgary.ca/computational-neuroscience/NeuromorphicComputing [ https://hbi.ucalgary.ca/computational-neuroscience/NeuromorphicComputing] *Qualifications:* Applicants must have a PhD or equivalent in a relevant domain (Electrical/Biomedical Engineering, Physics, Computer Science, Mathematics, Applied Mathematics or relevant adjacent fields) and experience relevant to one of the topic areas above. Some prior post-doctoral research is an asset but not required. *Application Details:* Applications should include a cover letter outlining research experience and interests, a CV and names of three potential references. Please email materials to: wilten.nicola(a)ucalgary.ca. Applications will be reviewed as they are received up until the deadline (November 30th 2026). The terms and conditions of employment are covered under the UCalgary and PDAC Collective Agreement. To find out more about postdoctoral scholar program at the University of Calgary visit our Postdocs website <https://research.ucalgary.ca/postdocs>. https://careers.ucalgary.ca/jobs/18230209-postdoctoral-scholar-in-the-neuro…

Oct. 16, 2026

Funded PhD Opportunities at the University of Toronto: NeuroAI and Computational Neuroscience

by Helena Liu

Funded PhD Opportunities: NeuroAI and Computational Neuroscience at the University of Toronto I am recruiting PhD students to join my new lab at the University of Toronto for Fall 2027. We study how brains and artificial neural networks learn from experience, form useful representations, assign credit to past events, and generalize to new situations. Our work combines computational neuroscience, machine learning, mathematical modeling, and neural and behavioral data, and has appeared in PNAS, NeurIPS, ICLR, and ICML. Research directions include how biological mechanisms support learning from delayed feedback; how neural circuit structure and learning dynamics shape representations, generalization, and continual learning; and methods for inferring learning processes from neural and behavioral data. As one of the founding students, you can begin with a concrete project in one of these directions, with room to bring your own interests into the work and help shape the lab’s culture. You can learn more about our work at helena-yuhan-liu.github.io<https://helena-yuhan-liu.github.io/>. Who should apply: Students from computer science, engineering, statistics, physics, applied mathematics, neuroscience, psychology, and related fields are encouraged to apply. A strong quantitative background and experience with scientific programming are important; applicants should be comfortable with linear algebra, probability/statistics, and computational problem solving. Prior training in neuroscience or psychology is not required. We especially value students who are intellectually curious, creative, self-motivated, and able to take ownership of a project and carry it through to completion. What we offer: Close, direct mentorship with individualized support for research and career development; interdisciplinary training in computational methods and scientific thinking; and opportunities to present at conferences and publish in machine-learning and neuroscience venues. Students will benefit from U of T’s strengths in AI, neuroscience, and cognitive science, with opportunities to connect and collaborate across disciplines. Toronto combines a vibrant research community with diverse neighborhoods, a lively arts scene, and excellent food. Admission and funding: This is a funded PhD opportunity through U of T’s Psychology graduate program. The program uses direct admission to a supervisor’s lab. Direct entry from a bachelor’s degree is available for eligible applicants; applicants with a master’s degree are also welcome. The application deadline is December 1, 2026. Please see the program’s application page<https://www.psych.utoronto.ca/prospective-graduate-students/application-inf…> for application requirements and instructions. Interested? Please apply through the U of T Psychology PhD program and list me as a prospective supervisor. If you have questions about research fit, feel free to email me with your CV attached. Best, Helena Liu ________________________________________ Y. Helena Liu Postdoctoral Research Associate, Princeton University Incoming Assistant Professor (January 2027), Department of Psychology, University of Toronto Website: https://helena-yuhan-liu.github.io/

Oct. 15, 2026