Arterial spin labeling (ASL) MRI has become a vital tool in clinical neuroimaging, enabling noninvasive assessment of cerebral perfusion across a range of conditions including stroke, vascular malformations, and brain tumors. With broader clinical adoption, its practical strengths — as well as important limitations — have become increasingly clear.

This project aims to explore the adaptation of large language models (LLMs), such as ChatGPT, to study their potential in understanding human language and identifying associated pathologies. By focusing on advanced neurocomputational models and the use of functional MRI, this work aims to decipher linguistic representations and their individual variations, particularly in pathological contexts such as dyslexia.

A fully funded 3-years PhD position (EU MSCA-COFUND program) is available at Aix-Marseille University (France) for motivated students interested in the behavioral, neurophysiological and computational investigation of multistable visual perception in healthy and pathological populations. The project is strongly cross-disciplinary, including psychophysical and oculomotor experiments as well as advanced computational modeling. It will also involve an international mobility at the University of Edinburgh (UK), as well as a collaboration with the psychiatry department of Lille Hospital (France).

There is an open postdoc position to work on a collaborative project with Rik Henson and Emily Holmes at the Emotional Cognition Lab at the University of Cambridge. This post will involve conducting advanced memory modelling.

The School of Psychology at the University of East Anglia has two lecturer / assistant professor posts available. We welcome applications in all areas of neuroscience – come join our outstanding faculty! We have great resources here at UEA (fNIRS, EEG, MRI, TMS, virtual reality, EyeLink 1000+, Tobii eye-trackers, mobile eye-trackers), including the newly established UEA Wellcome-Wolfson Brain Imaging Centre.

The FOX team of the CRIStAL laboratory (UMR CNRS), Lille, France, and the PR team of the MIS Laboratory, Amiens, France, are looking to recruit a post-doc starting as soon as possible and a joint PhD student starting in October 2025 in the field of asynchronous computer vision from event cameras. The main objective is to develop new models of spiking neural networks (SNN) capable of directly processing visual information in the form of spike trains. The proposed models must be validated experimentally on dynamic vision databases, following standard protocols and best practices. The PhD candidate will be funded for 3 years (grant application pending) and is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits. The post-doc will be hired for 18 months starting from March 2025 (this is a fully-funded position). The monthly gross salary is around 2500-3000€, including benefits.

The AI Department of the Donders Centre for Cognition (DCC), embedded in the Donders Institute for Brain, Cognition and Behaviour, and the School of Artificial Intelligence at Radboud University Nijmegen are looking for a researcher in reinforcement learning with an emphasis on safety and robustness, an interest in natural computing as well as in applications in neurotechnology and other domains such as robotics, healthcare and/or sustainability. You will be expected to perform top-quality research in (deep) reinforcement learning, actively contribute to the DBI2 consortium, interact and collaborate with other researchers and specialists in academia and/or industry, and be an inspiring member of our staff with excellent communication skills. You are also expected to engage with students through teaching and master projects not exceeding 20% of your time.

The Faculty of Psychology and Educational Sciences of the University of Coimbra (FPCE-UC) Portugal invites applications from rising and aspiring leaders in Computational Cognitive Neuroscience for 1 Assistant Professor position in Computational Cognitive Neuroscience. These positions are part of a transformative ERA Chair grant CogBooster from the European Union to FPCE-UC led by Dr. Alfonso Caramazza. The goal of CogBooster is to implement strong and international lines of research in Cognitive Science/Neuroscience, so as to contribute to the ongoing renewal of the Psychological and Brain Sciences in Portugal over the next decade. The project has recently led to the appointment of Jorge Almeida, Jason Fischer and Joana Carvalho as tenured Professors, and Alfonso Caramazza as a Visiting Researcher at the University of Coimbra. The University of Coimbra is an equal opportunity/affirmative action employer and has a Gender Equality Plan in place. We are committed to fostering a diverse and inclusive academic global community at the University. We particularly encourage applications from women, and from under-represented groups in the University of Coimbra’s workforce and in the brain sciences.

The PostDoctoral researcher will conduct research activities in modelling and simulation of reward-modulated prosocial behavior and decision-making. The position is part of a larger effort to uncover the computational and mechanistic bases of prosociality and empathy at the behavioral and circuit levels. The role involves working at the interface between experimental data (animal behavior and electrophysiology) and theoretical modelling, with an emphasis on Multi-Agent Reinforcement Learning and neural population dynamics.

The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), hosted by the University of São Paulo (USP), Brazil, and funded by the São Paulo Research Foundation (FAPESP), is offering two post-doctoral fellowships for recent PhDs with outstanding research potential. The fellowship will involve collaborations with research teams and laboratories associated with NeuroMat, strictly related to ongoing research lines developed by NeuroMat. The project may be developed at the laboratories of USP, campuses of São Paulo or Ribeirão Preto, or at UNICAMP, Campinas, in person. We seek candidates capable of developing independent research in one of the research lines below. 1. Stochastic modeling of neurobiological data. Candidates must have a strong background in probability theory with emphasis on stochastic processes. 2. Development of new statistical methods for neurobiological data. Candidates must have a strong background in statistics and data analysis and knowledge of computer programming. 3. Acquisition, processing, and quantitative analysis of neurobiological data. Candidates must have a strong background in neuroscience with previous experience in neurophysiological data acquisition, processing and analysis, and knowledge of computer programming. 4. Instrumentation development associated with brain stimuli. Candidates must have a strong background in electronic instrumentation, robotics, programing, and safety in medical equipment. The fellowship is competitive at an international level, and fellows benefit from extra funds for travel and research expenses plus limited support for relocation expenses.

The Centre for Artificial Intelligence and Neuroscience at University of Bonn is looking to recruit a postdoctoral fellow and a PhD student to undertake high quality research and produce high-impact publications in a collaborative research project investigating the mechanisms of human aversive learning (a pre-clinical model of anxiety disorders) in a big-data approach. The project is led by Prof Dominik Bach. The goal of the advertised positions is to (a) conduct an fMRI mega-analysis of multi-voxel threat representations, using existing data from the ENIGMA consortium, and (b) adapt and improve methods to infer threat learning from psychophysiological and behavioural data in fMRI and ambulatory settings. The position involves a close collaboration with the Department for Imaging Neuroscience (FIL) at UCL Queen Square Institute of Neurology, London, UK, where part of the team is based. The role includes implementing fMRI analysis pipelines in HALFpipe, coordination with participating sites to obtain and process data, mega-analysis of threat representation geometry, implementing and developing psychophysiology and behavioural analysis pipelines using PsPM/Matlab, CogLearn/VRthreat/R, and statistical modelling (e.g. Rstan), and publication of research and development results.

We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.