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

PhD opportunity in Computational Neuroscience at Jülich Research Center for US candidates

by Sacha van Albada

Dear all, We have a available a PhD opportunity in Computational Neuroscience at the Jülich Research Center, Germany, for candidates from the USA contingent on a successful grant application. In the context of an existing CRCNS (Collaborative Research in Computational Neuroscience) project between the Allen Institute (PI: Stefan Mihalas) and the Jülich Research Center (PIs: Markus Diesmann and Sacha van Albada), the German ministry BMFTR offers candidates from the USA an opportunity for a ‘Hop-On Fellowship’ to do a PhD at the Jülich Research Center. This entails a grant proposal written by the German partner along with resumes and letters of intent from candidates who have studied or done research in the USA at least for the past three years. The CRCNS project concerns the development of multi-area models of the mouse thalamocortical visual system, bringing together biological fidelity and visual function. To this end, the rich anatomical and physiological datasets from the Allen Institute are combined with expertise on modeling large-scale spiking neural network models from the Jülich partner, and functional properties are incorporated using advanced methods for training neural networks. The closely related PhD project (contingent upon the awarding of the Hop-On Fellowship) will take a top-down approach to enhance the biological realism of a functional model of the mouse visual cortex and gain insight into relationships between cortical structure, dynamics, and function. The candidate would be embedded in the Institute for Advanced Simulation (IAS-6, Computational and Systems Neuroscience, https://www.fz-juelich.de/en/ias/ias-6) at the Jülich Research Center and would have the opportunity to work together with the PIs and postdocs in the USA and Germany. The fellowship covers not only the salary of the PhD candidate but also travel costs to and from Germany. During the PhD, it is possible to work from the USA for up to five months in total (max. two months a year). A contribution to childcare costs in Germany of up to 150 euro a month is also available. To apply, please send your CV, copies of your Bachelor’s and Master’s transcripts, and a short motivation letter to Prof. Sacha van Albada, s.van.albada(a)fz-juelich.de<mailto:s.van.albada@fz-juelich.de> by August 10th, 2026. -- Prof. Sacha Jennifer van Albada Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience Jülich Research Centre Jülich, Germany Computational Neuroanatomy Institute of Zoology University of Cologne Cologne, Germany ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Dr. Stephanie Bauer (stellv. Vorsitzende), Prof. Dr. Ir. Pieter Jansens, Prof. Dr. Laurens Kuipers ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

July 30, 2026

Call for Papers: Dendritic Influence on Neuronal Input-Output Transformation Dynamics

by Craig Kelley

We are currently seeking submissions for a *Frontiers in Cellular Neuroscience Research Topic *titled *Dendritic Influence on Neuronal Input-Output Transformation Dynamics*. 1. Manuscript submission deadline is *30 December 2026*. 2. The goal of this Research Topic is to delve deeper into the complex dynamics of neurons, specifically focusing on the properties and functions of dendrites. The aim is to understand how the morphology and biophysical properties of dendrites influence the transformation of synaptic input into action potential output. This includes investigating the impact of dendritic location, timing, strength, and presynaptic origin on this transformation, as well as the role of different input populations in driving observed action potentials. The research also aims to explore how variability in synaptic input and biophysical and morphological properties affects neuronal responses, and how these factors contribute to the overall function and performance of the neural network. To gather further insights into the complex dynamics of neurons and the role of dendrites, we welcome articles addressing, but not limited to, the following themes: • The impact of dendritic location, timing, strength, and presynaptic origin on the transformation of synaptic input into action potential output. • The role of different input populations in driving observed action potentials. • The influence of synaptic input from various sources on neuronal responses. • The effect of variability in synaptic input and biophysical and morphological properties on neuronal responses. • The contribution of these factors to the overall function and performance of the neural network. • The development and application of computational models to study these dynamics and properties. • Linking dendritic dynamics to behavior. • The role of energetics/metabolism in regulating/constraining dendritic function. For more information, please visit https://www.frontiersin.org/research-topics/80722/dendritic-influence-on-ne… .

July 27, 2026

Postdoc opportunity at Northwestern University Medical School campus in Chicago USA

by Christina Zelano

Northwestern University’s Human Olfaction Lab is seeking a postdoctoral scholar to join their research team. The lab studies systems neuroscience, including the human olfactory system, cognition and respiration. The lab uses a wide range of human neuroscience methods, including functional neuroimaging, diffusion tensor imaging, intracranial EEG, electrical stimulation, and a new intranasal recording method that we recently developed that allows for high-precision electrophysiological recordings from the human olfactory bulb in healthy volunteers, and fast-scan-cyclic voltammetry based electrochemistry recordings from the olfactory epithelium. The lab is conducting experiments in healthy normosmic individuals, and also in disease populations including Anosmia, Alzheimer's, Parkinson’s and epilepsy. We are seeking an outstanding and highly motivated postdoc for this position. Principal Accountabilities 1. To conduct and analyze data from experiments on olfaction, respiration, cognition and or disease. 2. To develop new ideas, research methodologies, and analytic approaches to our lab’s research programs. 3. To help write grants and compete for funding. 4. To help write and contribute to publications resulting from research in the lab. Minimum Qualifications/Competencies: * Interest in research on olfaction and/or respiration and/or how these systems are affected in disease. * Computational skills (machine learning, RSA, pattern analysis, k-means clustering, modeling, etc) in analyzing neural data, preferably across a range of techniques including electrophysiology and neuroimaging. * Evidence of first-author peer-reviewed publications. * Excellent communication and writing skills Preferred Qualifications/Competencies: * Experience in human neuroscience research. * Experience in research and clinical training related to neurological conditions are preferred, but not a necessity. * Skillsets on computational, AI (broadly defined), and data science analytic approaches are an advantage. To Apply: Email a cover letter, cv, and 2 references contact info to c-zelano(a)northwestern.edu

July 26, 2026

Data Aide for NIH Funded Neuroimaging & AI Research at Stanford University

by daelsaid＠stanford.edu

Description of position: We are seeking a Data Aide 2 to join the laboratory of Dr. Supekar and support data organization, database management, and neuroimaging data preparation across multiple NIH-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 Data Aide will work closely with and under the direct oversight of Dr. Supekar on supporting the organization, management, and preparation of large-scale neuroimaging, clinical, cognitive, and molecular datasets focused on autism and schizophrenia-spectrum disorders. This is a full-time, in-person, computationally oriented role centered on data organization, database management, and neuroimaging data preparation. Core responsibilities include maintaining and managing REDCap databases, organizing and structuring neuroimaging data files for use in analysis, and ensuring data accuracy, completeness, and consistency across studies. The position offers hands-on experience supporting research at the intersection of neuroimaging, data science, and precision psychiatry, with close mentorship from the PI. Required Qualifications: • High school diploma, G.E.D., or equivalent and two years of relevant experience, or a combination of education and relevant experience. • Strong clerical, interpersonal, and basic organizational skills. • Good computer skills. • Ability to exercise judgment and employ basic reasoning skills. • Strong attention to detail. • Experience with REDCap or similar data management/database systems is desirable but not required. • Familiarity with programming languages such as Python, R, or MATLAB for data organization and preparation tasks is desirable but not required. • Interest in or familiarity with neuroimaging data and file structures is desirable but not required. Minimum Education and Experience Required: High school diploma, G.E.D., or equivalent and two years of relevant experience, or a combination of education and relevant experience. Required Application Materials: • Brief cover letter describing relevant experience, interest in the position, and fit with the lab • Current resume or CV • Contact information for two or three references Please email application materials as a single PDF to supekarlab(a)stanford.edu with the subject line: Data Aide. 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. Lab website: https://med.stanford.edu/supekar-lab.html

July 23, 2026

Call for candidates - Program Committee, annual Computational Neuroscience meeting, apply by July 24

by Rubchinsky, Leonid L

Dear Colleagues, We are looking for candidates for the Program Committee of the annual Computational Neuroscience meeting (https://www.cnsorg.org<https://www.cnsorg.org/>). The role entails inviting keynote speakers and overseeing the review process of submissions, as well as making the decisions on oral and poster presentations and other aspects of the program of the conference. This is a three year commitment. We are soliciting self-nominations of OCNS members of all career stages and research interests. Priority will be given to the applications received by July 24. Please email program(a)cnsorg.org<mailto:program@cnsorg.org?subject=CNS%20program%20committee%20application> if you are interested with a few sentences about yourself and your past involvement with the CNS meetings. Best regards, OCNS Program Committee

July 22, 2026

Postdoc position in computational modeling and neuro-prosthetics in Prague

by Jan Antolik

Dear Colleagues, We're pleased to announce a postdoctoral position within the Computational Systems Neuroscience Group (CSNG) at Charles University, Prague, focusing on computational neuroscience and neuro-prosthetic system design. CSNG is an international, interdisciplinary research group that explores the intricacies of the visual system, sensory coding, and neuro-prosthetic solutions. To do this, we use a range of computational approaches spanning large-scale biologically detailed spiking network models, firing-rate models of development, and modern machine learning techniques. We are a diverse team of computer scientists, physicists, mathematicians, and engineers dedicated to understanding visual perception and its restoration via neuro-prosthetic devices. Multiple project topics are available (the exact topic can be adjusted to the interest and background of the applicant): - Modeling electrical stimulation in a spiking model of the primary visual cortex. The goal of this project is to understand how intra-cortical stimulation engages the neural code of the visual cortex. - Deep-neural networks in visual neuroscience and possibly its applications for understanding of visual perception under artificial cortical stimulation. - Study of cortical dynamics of spontaneous or artificially evoked activity in the visual cortex. - Biologically detailed spiking large-scale models of early visual cortical pathway - from Retina to V4. Requirements: Strong computational and analytical skills. Experience in computational modeling, spiking neural networks, machine learning or understanding of electric field propagation in biological mediums are of great advantage but not all strictly required. Duration: 1 year contract with possibility of extension. The position is fully funded at least until the end of 2028. Funding: Fully funded Starting date: now Teaching duties: None Monthly salary (gross): ~2900€ To apply, send CV and cover letter explaining your interest and how your skills align with our project to: antolik(a)ksvi.mff.cuni.cz

July 21, 2026

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

by Lyle Muller

Postdoctoral and graduate research positions are available in the Muller Lab (http://mullerlab.io<http://mullerlab.io/>), starting August 2026 at the University of Texas at Dallas. Research will span computational analyses of neural data and applied mathematics to understand computation in neural networks. Researchers will collaborate with multi-disciplinary teams on traveling waves in neural systems (link<https://www.nature.com/articles/s41467-023-39076-2>), analyses of spikes in large neural populations (link<https://arxiv.org/abs/2412.03804>), and spatiotemporal computation in recurrent neural networks (link<https://www.nature.com/articles/s42005-024-01728-0>). The lab also has an ongoing presence at the Fields Institute through the Lab for Network Computation<http://www.fields.utoronto.ca/activities/fields-lab-network-computation>. Recent publications from the lab include a review on traveling waves in neural systems<https://authors.elsevier.com/c/1nTl93BtfHGow9>, focusing on circuit mechanisms for spatiotemporal organization of spiking and synaptic activity in single regions of cortex, and how traveling waves can generate computations in artificial neural networks modeling these systems. Prior experience with scientific computing, signal analysis, and applied mathematics is preferred. UT Dallas offers an outstanding research environment, with strong programs in systems and computational neuroscience. Candidates can write to lyle.muller(a)utdallas.edu<mailto:lyle.muller@utdallas.edu> with a CV and short statement of interest. -- Lyle Muller http://mullerlab.ca

July 21, 2026

Systems Vision Science (SVS) Virtual Symposium July 31 – Program available, register (free) to obtain zoom link

by Pavlovic, Maria

Systems Vision Science <https://www.lizhaoping.org/Systems-Vision-Science.html> Virtual Symposium: July 31st, 2026, 02.00 – 06.00 pm (german time) The program is now available here: https://summerschool.lizhaoping.org/symposium-2026/ To participate via zoom, please register <https://docs.google.com/forms/d/e/1FAIpQLSfCIzbDUeswiWyB39JE9VX7Jj-DYJ5ajh8…> FOR FREE. Registration is accepted until space is filled! -------------------------------- Keynote, invited, and contributed speakers of the Systems Vision Science Symposium include: 14:00-14:30 Wu Li: „Corticocortical Feedback as a Unifying Mechanism for Visual Perception and Learning” 14:30-14:45 Daniel Anthes: „The illusory simplicity of the feedforward sweep” 14:45-15:00 Tatia Buidze: „Outcome history shapes implicit perceptual learning without awareness of the eye of origin“ 15:00-15:15 Pinglei Bao: „Triple-N Dataset: Linking Single Neurons, Deep Neural Networks, and Natural Vision” 15:15-15:30 Lisa Kroell: „Predictive foveal processing in active vision“ 15:30-15:45 Winrich Freiwald: „From objects to action symbols“ 15:45-16:00 Virtual Coffee Break 16:00-16:15 Marlene Behrmann: „Understanding human vision through studying its breakdown“ 16:15-16:30 Li Zhaoping: „Ventral stream feedback to V1 is focused on the central visual field: Anatomical evidence for the looking-and-seeing framework of vision“ 16:30-16:45 Marisa Carrasco: „Dissecting Covert Attention: A Cortical Double Dissociation“ 16:45-17:00 Bruno Rossion: „The cellular basis of the human face inversion effect“ 17:00-17:15 Zygmunt Pizlo: „Mathematical foundations of Gestalt psychophysics.“ 17:15-17:30 Shahin Nasr: „Mesoscale developmental rivalry in human extrastriate visual cortex“ 17:30-18:00 Patrick Cavanagh: „Using Illusions and Art to Understand the Visual Brain“ -------------------------------- The zoom link for joining the symposium will be sent to the registrants. Please direct inquiries to <mailto:svs.summerschool@tue.mpg.de> svs.summerschool(a)tue.mpg.de. -------------------------------- Organizing Team Li Zhaoping Maria Pavlovic Vladislav Aksiotis Kai Rothe Max-Planck-Institute for Biological Cybernetics University of Tübingen

July 21, 2026

3 Fully Funded PhD Positions in AI/ML & Computational Brain Health @ Interdisciplinary Transformation University (IT:U) Austria

by Jie Mei

The newly established Interdisciplinary Transformation University (IT:U) in Linz, Austria, invites applications for 3 fully funded PhD positions within our PhD Program in Interdisciplinary Computing. The contract can be extended to 4 years to support full project completion. We are seeking highly motivated researchers with strong mathematical and technical backgrounds to develop cutting-edge AI/ML models specifically targeted at computational brain health, neurodegenerative diseases, and disorders of consciousness. The Positions & Projects 1 Position – PROCEED: Predicting Recovery Of Consciousness via Evidence-based Evaluation and Diagnosis. PIs: Dr. Jie Mei (IT:U) & Dr. Spiros Denaxas (IT:U / UCL). 2 Positions – INDEED-PD: Integration of Novel Biomarkers for Detection, Evaluation of Early Signatures, and Developmental Insights of Parkinson’s Disease. PIs: Dr. Jie Mei (IT:U), Dr. Daniel Klotz (IT:U), and Dr. Erich Kobler (Institute for Machine Learning, JKU). Key Responsibilities * Design and implement AI/ML architectures optimized for computer vision, neural signal processing, and time-series data. * Create experimental setups to evaluate computational methods against strong baselines. * Work closely across a network of computer scientists and clinicians (IT:U, JKU, and Kepler University Hospital). * Publish and present findings at top-tier international scientific AI/ML and neuroscience venues. What We Are Looking For * A master's degree in a highly quantitative discipline: Computer Science, Mathematics, Physics, Electrical Engineering, Statistics, or related fields. * Solid grounding in the theory and practice of Deep Learning. * Hands-on experience in AI/ML applications. * An interest in the computational modeling of brain circuitry in health and disease is a plus. View job postings here: https://it-u.at/en/study-program/doctoral-school/phd-interdisciplinary-comp… Best regards Jie Mei Assistant Professor of Computational Neuroscience [Ein Bild, das Schwarz, Dunkelheit enthält. Automatisch generierte Beschreibung] Altenberger Straße 66c Science Park 4 | OG 2 4040 Linz | Austria ATU79114879 www.it-u.at<https://www.it-u.at/> +43 676 / 851307 254 jie.<mailto:vorname.nachname@it-u.at>mei(a)it-u.at<mailto:vorname.nachname@it-u.at> Unsere Datenschutzerklärung finden Sie unter https://it-u.at/en/privacy/. Der Inhalt dieser E-Mail ist vertraulich und kann rechtlich besonders geschützte Informationen beinhalten. Wenn Sie nicht der beabsichtigte Empfänger sind, dürfen Sie diese E-Mail oder die angeschlossenen Dateien weder bearbeiten, verwenden, weitergeben noch reproduzieren. Sollten Sie nicht der vorgesehene Empfänger sein, ersuchen wir Sie höflich, sich sofort mit uns ins Einvernehmen zu setzen und die E-Mail und sämtliche angeschlossene Dateien zu löschen. You can find our Privacy Policy here https://it-u.at/en/privacy/. The content of this email is confidential and may also contain legally privileged information. If you are not the intended recipient you may not process, use, transmit or reproduce this email or any enclosed attachments. If you have received this email by mistake, please notify the sender immediately and delete this email and any enclosed attachments from your system.

July 20, 2026

2-year postdoc at Neurospin/INM, Paris: Computational and high field MRI characterization of learning and decision-making - Supervisor: Dr Florent Meyniel

by Zeineb Amor

Dear all, A two-year postdoctoral position is available at NeuroSpin/INM (Paris, France) under the supervision of Dr. Florent Meyniel. https://computationalbrainteam.com/en/recruitment/2-year-postdoctoral-posti… *Project* Computational and high field MRI characterization of learning and decision-making *Supervisor and contact* Dr Florent MEYNIEL https://www.unicog.org/lab/the-computational-brain/ *Duration and Dates* * Initial duration: two years * Extension possible * Full-time post * Preferred starting date: October 2026 to January 2027 *Project description* Learning and decision making are intertwined processes in many everyday situations. One example is when you decide where to have lunch: should you go to the nearby coffee shop or to the university cafeteria? Learning depends on choice, because you can learn which option you prefer by trying each option repeatedly, and decision making depends on learning, because you eventually want to select the option you have learned you like best. Uncertainty plays a key role in both learning1–4 and decision making5, especially when the environment is not stationary (e.g., a new brand now runs the nearby coffee shop and you like it less). In the ANR/CEA-funded BrainSync collaborative project, we are interested in characterizing the neural representations of uncertainty6–8 and value that emerge from learning and guide decisions. Our approach combines deep phenotyping approach and multimodal imaging in the EXPLORE task5. We collected data from 16 participants who participated in one behavioral session, two 7T fMRI sessions, and two MEG sessions. 56 other participants participated in one pupillometry session and one 3T fMRI session. We also plan to record a dataset on our 11.7T MRI, and we have started to collect intracranial EEG data. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD student is working on the behavioral and 11.7T data (to be collected). We are looking for another postdoc for the already collected fMRI datasets. The postdocs and PhD student will work together to perform analyses informed by all datasets. *Profile* Ph.D. in neuroscience, machine learning or psychology, with strong programming skills, ideally in Python. Previous experience with ideally fMRI, computational modeling. You will be responsible for analyzing fMRI data and disseminating results in internal seminars, international conferences and journal articles. The working language of the lab is English. French is not required. *Workplace and environment* Dr Florent MEYNIEL leads the Computational Brain team (more info here), which is located in two places. * Institute of NeuroModulation (INM), Sainte Anne Hospital, Paris, France. The INM is part of the GHU Paris, Psychiatry & Neurosciences. The INM combines clinical activities and innovative clinical research in psychiatry with basic research in computational neuroscience. Team members spend most of their days here. * NeuroSpin, Paris-Saclay Campus, France. NeuroSpin is part of the CEA (Commissariat à l'Energie Atomique). Directed by Prof. Stanislas DEHAENE, NeuroSpin is a world-class brain imaging center equipped with a MEG system (Elekta, Neuromag) and several human MRI scanners (3T Prisma, 7T and 11.7T), all for research purposes only. The community at NeuroSpin is very scientifically stimulating, combining MRI physicists, machine learning experts, and cognitive neuroscientists. Team members go there to collect data and collaborate with their colleagues in the Cognitive NeuroImaging Unit. *Application procedure* Please send an email to Florent Meyniel (florent.meyniel(a)cea.fr) * Your CV * a research statement describing your research interests and proposed directions * the contact details of two referees Applications will be considered on a rolling basis (positions will remain open until filled). *Salary * Will follow INM standards and depend on experience. *References* 1. Meyniel, F., Schlunegger, D. & Dehaene, S. The Sense of Confidence during Probabilistic Learning: A Normative Account. PLoS Comput Biol 11, e1004305 (2015). 2. Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015). 3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities. Open Mind 8, 615–638 (2024). 4. Meyniel, F. Brain dynamics for confidence-weighted learning. PLOS Comput. Biol. 16, e1007935 (2020). 5. Paunov, A. et al. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. 2024.03.27.587016 Preprint at https://doi.org/10.1101/2024.03.27.587016 (2024). 6. Walker, E. Y. et al. Studying the neural representations of uncertainty. Nat. Neurosci. 1–11 (2023) doi:10.1038/s41593-023-01444-y. 7. Bounmy, T., Eger, E. & Meyniel, F. A characterization of the neural representation of confidence during probabilistic learning. NeuroImage 268, 119849 (2023). 8. Meyniel, F. & Dehaene, S. Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Proc. Natl. Acad. Sci. 201615773 (2017) doi:10.1073/pnas.1615773114. Best regards, Zaineb Amor, PhD Research Engineer GHU Paris, Psychiatrie et Neurosciences Institut de Neuromodulation Paris, France

July 16, 2026