Applications are invited for a postdoctoral position investigating the computational neuroscience and computational psychiatry of cannabinoids on learning and decision-making. The position is funded as part of the “CANNABODIES” project, a 5 year European Research Council (ERC) Starting Grant awarded to Micah Allen, principal investigator of the Embodied Computation Group (link: https://www.the-ecg.org/). The candidate will have the unique chance to work at the forefront of computational modelling, neuroimaging, and cannabinoid research in a variety of different decision-making modalities, both locally and together with our international partners. The position is a full-time position and funded for an initial duration of 2 years, and can be extended up to a total of 4 years following an initial probationary period. Eligible candidates are expected to start February 1st, 2022, or as soon as possible thereafter.

The 'Developmental Computational Psychiatry' lab and the W3 professorship 'Computational Psychiatry' led by Tobias Hauser at the University of Tübingen (Germany) is currently hiring new postdocs. The focus of the lab is to better understand the computational and neural mechanisms underlying decision making and learning, and how these processes go awry in patients with mental illnesses. The successful candidates will have the chance to work in a highly dynamic and inspiring environment and to collaborate closely with Prof Peter Dayan and the Max Planck Institute for Biological Cybernetics.

The 'Developmental Computational Psychiatry' lab and the W3 professorship 'Computational Psychiatry' led by Tobias Hauser at the University of Tübingen is hiring new postdocs. The lab focuses on understanding the computational and neural mechanisms underlying decision making and learning, and how these processes are affected in patients with mental illnesses. Successful candidates will work in a dynamic environment and collaborate with Prof Peter Dayan and the Max-Planck Institute for Biological Cybernetics.

The Carney Institute for Brain Science at Brown University is seeking Postdoctoral Fellows to join the NIMH funded T32 Training Program in Computational Psychiatry. The program’s goal is to train research fellows with expertise in computational cognitive and systems neuroscience, capable of collaborating with clinical researchers to advance knowledge of psychiatric disorders and treatments. Eligible research topics include brain and cognitive modeling over multiple scales and levels of analysis (ranging from biophysics to artificial intelligence), and the use of these models to understand mechanisms of psychiatric disorders with the ultimate goal of improving treatments. The program applies an apprenticeship model in which fellows work with a primary research trainer in a computational field and a secondary research mentor in clinical psychiatry. In this apprenticeship model, the trainer works closely with the fellow and a secondary clinical psychiatry mentor, who is conducting research in areas such as neuroimaging, neurostimulation, digital phenotyping, and/or animal models. The list of eligible faculty trainers can be found on the Training Program in Computational Psychiatry’s website.

The 'Developmental Computational Psychiatry' lab and the W3 professorship 'Computational Psychiatry' led by Tobias Hauser at the University of Tübingen (Germany) is currently hiring new postdocs. The focus of the lab is to better understand the computational and neural mechanisms underlying decision making and learning, and how these processes go awry in patients with mental illnesses. The successful candidates will have the chance to work in a highly dynamic and inspiring environment and to collaborate closely with Prof Peter Dayan and the Max-Planck Institute for Biological Cybernetics. Concretely, we are looking for the following candidates: Postdoc with experimental & neuroimaging background, Postdoc with computational modelling background. More information about the positions can be found here: https://devcompsy.org/join-the-lab/. Interested candidates are encouraged to reach out to Tobias Hauser directly to informally discuss the positions.

📢 Join the Lin Lab at Mount Sinai! We’re Hiring Postdocs, Research Assistants, and PhD Students! The Lin Lab, also known as the Bytes of Minds Lab, is on the lookout for driven researchers passionate about Computational Psychiatry and Neuro-AI. Directed by Dr. Baihan Lin (me!) and based at the Icahn School of Medicine at Mount Sinai, New York’s largest hospital network, our lab is uniquely positioned with access to vast data resources and a strong collaborative environment. We’re pushing the boundaries of mental health technology and brain-inspired AI to create intelligent systems that can transform healthcare and deepen our understanding of the mind. Why Join Us? 🍎 Cutting-edge Research: Tackle challenges in neuro-inspired AI, computational psychiatry, brain-computer interfaces, extended realities (XR), social media, wearables, and beyond. 🍎 Interdisciplinary Impact: Work at the intersection of advanced neuroscience, machine learning, and cognitive science to create adaptive AI systems, new tools for mental health, and next-gen neurotechnology. 🍎 Top-Tier Environment: Join Mount Sinai’s dynamic research community, within New York’s largest health system with the most diverse patient populations and a leading hub for AI in healthcare (ranked #1 by Nature). Whether you're a potential postdoc, PhD student, or someone looking for an interdisciplinary research experience, if you’re passionate about bridging the gap between bytes and minds, we want to hear from you! Learn more at linlab.org and apply by emailing me at baihan.lin@mssm.edu. Bytes of Minds Lab (Lin Lab) Departments of AI, Psychiatry, and Neuroscience Hasso Plattner Institute for Digital Health, Friedman Brain Institute, Center for Computational Psychiatry

A key insight from the foraging literature is the importance of assessing the overall environmental quality — via global reward rate or similar measures, which capture the opportunity cost of time and can guide behavioral allocation toward relatively richer options. Meanwhile, the majority of research in decision neuroscience and computational psychiatry has focused instead on how choices are guided by much more local, event-locked evaluations: of individual situations, actions, or outcomes. I review a combination of research and theoretical speculation from my lab and others that emphasizes the role of foraging's average rewards and opportunity costs in a much larger range of decision problems, including risk, time discounting, vigor, cognitive control, and deliberation. The broad range of behaviors affected by this type of evaluation gives a new theoretical perspective on the effects of stress and autonomic mobilization, and on mood and the broad range of symptoms associated with mood disorders.

Psychotic disorders are devastating conditions without any mechanistic treatment available. One major hurdle in the biological study of psychosis is the challenge of rigorously probing this condition in pre-clinical animal models. The goal of our research is to develop and exploit innovative frameworks for the study of psychosis in mice. In our present work, where we developed a cross-species computational psychiatry approach to probe hallucination-like perception. This enabled us to directly relate human and mouse behavior, and to demonstrate and dissect the causal role of striatal dopamine in hallucination-like perception. Our results suggest a neural circuit mechanism for the long-standing dopamine hypothesis of psychosis, and provide a new translational framework for the biological study of psychosis. This opens up exciting possibilities for advancing the biological understanding of psychosis and to identify mechanistic treatment targets.

Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern theoretical approaches to understanding and mitigating risk in either one-shot or sequential settings, which are derived largely from finance and economics, have yet to permeate fully the fields of neural reinforcement learning and computational psychiatry. I will discuss the use of dynamic and static versions of one prominent approach, namely conditional value-at-risk, to examine both the nature of risk avoidant choices, encompassing such things as justified gambler's fallacies, and the optimal planning that can lead to consideration of such choices, with implications for offline, ruminative, thinking.