The M.Sc. program 'Modeling for Neuronal and Cognitive Systems' at Université Côte d'Azur is recruiting new students. This international Master of science is interdisciplinary and related to computational approaches in neuroscience/cognitive science.

The Diedrichsen Lab is looking to recruit a new postdoctoral associate with interest in studying the human cerebellum. The successful candidate will join an inter-disciplinary research group that uses behavioral, neuroimaging, and computational approaches to investigate the role of the cerebellum in motor control, cognition, and language. Our approach is to use Big Data and Machine Learning to gain insight about the overall functional organization of the cerebellum, which then informs targeted imaging and behavioural experiments. The enthusiastic and supportive environment will enable the candidate to develop their own research program.

We are seeking postdoctoral fellows with interest and experience in computational approaches for quantifying human social behavior. This research is conducted at the University of Pennsylvania and the Center for Autism Research at Children’s Hospital of Philadelphia, as a part of multiple NIH grants. The applicant will be part of a big multidisciplinary team that develops AI tools to study human behavior (facial and bodily movements) during social interactions. Our research is a unique blend of machine learning, computer vision, cognitive science, bioinformatics, and mental health conditions. The fellow will be responsible for all or some of the following tasks, depending on the expertise: - Developing computer vision techniques (e.g., face analysis, body movement analysis, gesture analysis) - Developing signal processing methodologies to analyze biological and behavioral signals (e.g., head movements, joint movements) - Developing time series analysis techniques to extract patterns in biological and behavioral signals (e.g., coordination and causality in movements of multiple people) - Validating developed tools using in-house clinical data, as well as publicly available datasets - Performing pattern recognition on collected data (i.e., classification, regression, clustering, feature learning)

We are seeking postdoctoral fellows with interest and experience in computational approaches for quantifying human social behavior. This research is conducted at the University of Pennsylvania and the Center for Autism Research at Children’s Hospital of Philadelphia, as a part of multiple NIH grants. The applicant will be part of a big multidisciplinary team that develops AI tools to study human behavior (facial and bodily movements) during social interactions. Our research is a unique blend of machine learning, computer vision, cognitive science, bioinformatics, and mental health conditions. The fellow will be responsible for all or some of the following tasks, depending on the expertise: Developing computer vision techniques (e.g., face analysis, body movement analysis, gesture analysis), Developing signal processing methodologies to analyze biological and behavioral signals (e.g., head movements, joint movements), Developing time series analysis techniques to extract patterns in biological and behavioral signals (e.g., coordination and causality in movements of multiple people), Validating developed tools using in-house clinical data, as well as publicly available datasets, Performing pattern recognition on collected data (i.e., classification, regression, clustering, feature learning)

We are currently recruiting both a research technician and a fully funded PhD student to work on a Wellcome funded project 'How does the brain map sounds into the world?'. This Wellcome funded project uses a range of systems neuroscience and computational approaches to understand how auditory space is constructed in freely moving animals that are pursuing audio and audiovisual targets. The PhD student will be paid as a research assistant for four years, and have their fees funded at the UK rate.

The project is based on our research (Ryom and Treves, Phys Rev X Life, 2023, and unpublished) and on extensive data on language parameters collected by Longobardi and collaborators (Ceolin et al, Phil Trans Roy Soc B, 2021) which suggest that language diversity may reflect the disordered freezing dynamics that in a spin glass leads to a multiplicity of ground states. In a simplified model, vectors of binary syntactic parameters interact via strong asymmetric logical implications and weak partly symmetric influences, which can produce critical slowing down through a novel mechanism of percolated freezing. Candidates should be keen to develop new mathematical and computational approaches, as sketched in https://arxiv.org/abs/2307.03152 or otherwise, and enjoy learning about issues and narratives in scientific communities other than their own.

The department of psychology at UC San Diego has an open assistant professor position focused on computational approaches to understanding behavior. More information can be found here: https://apol-recruit.ucsd.edu/JPF04049

The Department of Psychology at The Pennsylvania State University, University Park, PA, invites applications for a full-time Assistant or Associate Professor of Cognitive Psychology with anticipated start date of August, 2025. Areas of specialization within cognitive psychology are open and may include (but are not limited to) such topics as cognitive control, creativity, computational approaches and modelling, motor control, language science, memory, attention, perception, and decision making. A record of collaboration is desirable for both ranks. Substantial collaboration opportunities exist within the department that align with the department’s cross-cutting research themes and across campus. Current faculty in the cognitive area are active in units including the Center for Language Sciences, the Social Life and Engineering Sciences Imaging Center, the Center for Healthy Aging, the Center for Brain, Behavior, and Cognition and the Applied Research Lab. Responsibilities of the Assistant or Associate Professor of Cognitive Psychology include maintaining a strong record of publications in top outlets. This position will include resident instruction at the undergraduate and graduate level and normal university service, based on the candidate’s qualifications. A Ph.D. in Psychology or related field is required by the appointment date for both ranks. Candidates for the tenure-track Assistant Professor of Cognitive Psychology position must have demonstrated ability as a researcher, scholar, and teacher in a relevant field and have evidence of growth in scholarly achievement. Duties will involve a combination of teaching, research, and service, based on the candidate’s qualifications. Candidates for the tenure-track Associate Professor of Cognitive Psychology position must have demonstrated excellence as a researcher, scholar, and teacher in a relevant field and have an established reputation in scholarly achievement. Duties will involve a combination of teaching, research, and service, based on the candidate’s qualifications. The ideal candidate will have a strong record of publications in top outlets and have a history of or potential for external funding. In addition, successful candidates must either have demonstrated a commitment to building an inclusive, equitable, and diverse campus community, or describe one or more ways they would envision doing so, given the opportunity. Review of applications will begin immediately and will continue until the position is filled. Interested candidates should submit an online application at Penn State’s Job Posting Board, and should upload the following application materials electronically: (1) a Cover letter of application, (2) Concise statements of research and teaching interests, (3) a CV and (4) three selected (re)prints. System limitations allow for a total of 5 documents (5mb per document) as part of your application. Please combine materials to meet the 5-document limit. In addition, please arrange to have three letters of recommendation sent electronically to PsychApplications@psu.edu with the subject line: “Cognitive Psychology” Questions regarding the application process can be emailed to PsychApplications@psu.edu and questions regarding the position can be sent to the search chair: cogsearch@psu.edu. The Pennsylvania State University is committed to and accountable for advancing diversity, equity, and inclusion in all of its forms. We embrace individual uniqueness, foster a culture of inclusion that supports both broad and specific diversity initiatives, leverage the educational and institutional benefits of diversity, and engage all individuals to help them thrive. We value inclusion as a core strength and an essential element of our public service mission. Penn State offers competitive benefits to full-time employees, including medical, dental, vision, and retirement plans, in addition to 75% tuition discounts (including for a spouse and dependent children up to the age of 26) and paid holidays.

In our rapidly evolving digital world, there is increasing concern about the impact of digital technologies and social media on the mental health of young people. Policymakers and the public are nervous. Psychologists are facing mounting pressures to deliver evidence that can inform policies and practices to safeguard both young people and society at large. However, research progress is slow while technological change is accelerating.My talk will reflect on this, both as a question of psychological science and metascience. Digital companies have designed highly popular environments that differ in important ways from traditional offline spaces. By revisiting the foundations of psychology (e.g. development and cognition) and considering digital changes' impact on theories and findings, we gain deeper insights into questions such as the following. (1) How do digital environments exacerbate developmental vulnerabilities that predispose young people to mental health conditions? (2) How do digital designs interact with cognitive and learning processes, formalised through computational approaches such as reinforcement learning or Bayesian modelling?However, we also need to face deeper questions about what it means to do science about new technologies and the challenge of keeping pace with technological advancements. Therefore, I discuss the concept of ‘fast science’, where, during crises, scientists might lower their standards of evidence to come to conclusions quicker. Might psychologists want to take this approach in the face of technological change and looming concerns? The talk concludes with a discussion of such strategies for 21st-century psychology research in the era of digitalization.

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

The last twenty years have witnessed extraordinarily rapid progress in basic neuroscience, including breakthrough technologies such as optogenetics, and the collection of unprecedented amounts of neuroimaging, genetic and other data relevant to neuroscience and mental health. However, the translation of this progress into improved understanding of brain function and dysfunction has been comparatively slow. As a result, the development of therapeutics for mental health has stagnated too. One central challenge has been to extract meaning from these large, complex, multivariate datasets, which requires a shift towards systems-level mathematical and computational approaches. A second challenge has been reconciling different scales of investigation, from genes and molecules to cells, circuits, tissue, whole-brain, and ultimately behaviour. In this talk I will describe several strands of work using mathematical, statistical, and bioinformatic methods to bridge these gaps. Topics will include: using artificial neural networks to link the organization of large-scale brain connectivity to cognitive function; using multivariate statistical methods to link disease-related changes in brain networks to the underlying biological processes; and using network-based approaches to move from genetic insights towards drug discovey. Finally, I will discuss how simple organisms such as C. elegans can serve to inspire, test, and validate new methods and insights in networks neuroscience.

In my talk I will discuss the concept of a cognitive ontology, which defines the parts of the mind that psychologists and neuroscientsts aim to study. I will discuss the way in which ontologies have traditionally been defined, and then discuss ways in which ontology might be reconsidered in the context of computational approaches to cognition.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.

Join the Department of Bioengineering on the 26th May at 9:00am for The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium. This year’s lecture speaker will be distinguished bioengineer and neuroscientist Professor Mandyam V. Srinivasan AM FRS, from the University of Queensland. Professor Srinivasan studies visual systems, particularly those of bees and birds. His research has revealed how flying insects negotiate narrow gaps, regulate the height and speed of flight, estimate distance flown, and orchestrate smooth landings. Apart from enhancing fundamental knowledge, these findings are leading to novel, biologically inspired approaches to the design of guidance systems for unmanned aerial vehicles with applications in the areas of surveillance, security and planetary exploration. Following Professor Srinivasan’s lecture will be the Bioinspired GNC Mini Symposium with guest speakers from Google Deepmind, Imperial College London, the University of Würzburg and the University of Konstanz giving talks on their research into autonomous robot navigation, neural mechanisms of compass orientation in insects and computational approaches to motor control.

All sensory perception and every coordinated movement, as well as feelings, memories and motivation, arise from the bustling activity of many millions of interconnected cells in the brain. The ultimate function of this elaborate network is to generate behavior. We use zebrafish as our experimental model, employing a diverse array of molecular, genetic, optical, connectomic, behavioral and computational approaches. The goal of our research is to understand how neuronal circuits integrate sensory inputs and internal state and convert this information into behavioral responses.