We are looking for a highly motivated researcher to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain during multi-modal language comprehension. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain. The projects are conducted in collaboration with UC Berkeley.

We have a postdoc position at the Max Planck UCL Centre for Computational Psychiatry and Ageing Research and the Wellcome Centre for Human Neuroimaging to fill this summer. The eligible candidate should have a strong background in fMRI and decision making. They will join the developmental computational psychiatry group, working on innovative topics, such as structure learning, complex decision making and mental health. The focus will be on conducting fMRI research with the possibility to do computational modelling.

The eligible candidate should have a strong background in fMRI and decision making. He will join the developmental computational psychiatry group, working on innovative topics, such as structure learning, complex decision making and mental health. The focus will be on conducting fMRI research with the possibility to do computational modelling.

We are offering a fully funded PhD opportunity to examine the interplay between attention and decision-making in complex, naturalistic tasks using advanced electro-/magnetoencephalography (E/MEG) techniques. Strong quantitative skills will be advantageous for this project. The project will be co-supervised by Dr. Konstantinos Tsetsos (University of Bristol) and Professor Anina Rich (Macquarie University). The selected student will join a unique PhD cohort, as part of a cotutelle graduate program between the University of Bristol and Macquarie University.

We are looking for a highly motivated researcher to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain during language comprehension. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain across languages.

A fixed-term research position is open for a post-doc, or for a PhD student nearing the end of his doctoral program. The goal of the research is to study hybrid collective intelligence systems for decision support in complex open-ended problems. It involves the design and implementation of a hybrid collective intelligence system to exploit the interaction between human experts and artificial agents based on knowledge graphs and ontologies for knowledge representation, integration and reasoning.

The Department of Engineering Mathematics at the University of Bristol is seeking an outstanding candidate to fill the role of Professor in Artificial Intelligence. You will have the opportunity to provide visionary leadership to the department and its staff, students, & partners, helping to strengthen and further develop our already impressive research and teaching programs in AI. Our Intelligent Systems Group supports the Faculty of Engineering's AI/Data Science Theme, fostering an inclusive environment for all.

We are looking for an excellent candidate with a master’s degree in MSc in Artificial Intelligence, Computer Science, Mathematics, Statistics, or a closely related field to join a project focused on developing an advanced transparent machine learning framework with application on movement behavioural analysis. Smartwatches and other wearable technologies allow us to continuously collect data on our daily movement behaviour patterns. We would like to understand how machine learning techniques can be used to learn causal effects from time-series data to identify and recommend effective changes in daily activities (i.e., possible behavioural interventions) that are expected to result in concrete health improvements (e.g., improving cardiorespiratory fitness). This research, at the intersection of machine learning and causality, aims to develop algorithms for finding causal relations between behavioural indicators learned from the time series data and associated health-outcomes.

We are seeking a motivated postdoctoral researcher to work on an interdisciplinary project at the intersection of deep learning and comparative politics. The candidate will work in the Human-Centered Machine Learning (HuMaLearn) team of Prof. Benoît Frénay and the Belgian and Comparative Politics team of Prof. Jérémy Dodeigne. The goal will be to develop new deep learning methodologies to analyse large corpuses of archive videos that picture political debates. We specifically aim to detect emotions, body language, movements, attitudes, etc. This project is linked to the ERC POLSTYLE project that Jérémy Dodeigne recently obtained, guaranteeing a stimulating research environment. The HuMaLearn team gathers about ten researchers, many of them being actively working in deep learning, but not only and with a keen openness to interdisciplinarity.

We have a position for a postdoctoral research associate to work on NAS and AutoML with Dr Elliot J. Crowley and the Bayesian and Neural Systems Group. The successful applicant will be based in the School of Engineering at the University of Edinburgh, and will have opportunities for collaboration within and outside of the school e.g. with colleagues in the Institute for Digital Communications and the Bayesian and Neural Systems Group. This position is funded for 24 months (provisional start date: November 2023) and the salary is UE07 £36,333 - £43,155 Per Annum.

The postdoctoral researcher will work on an interdisciplinary project at the intersection of deep learning and comparative politics. The candidate will work in the Human-Centered Machine Learning (HuMaLearn) team of Prof. Benoît Frénay and the Belgian and Comparative Politics team of Prof. Jérémy Dodeigne. The goal will be to develop new deep learning methodologies to analyse large corpuses of archive videos that picture political debates. We specifically aim to detect emotions, body language, movements, attitudes, etc. This project is linked to the ERC POLSTYLE project that Jérémy Dodeigne recently obtained, guaranteeing a stimulating research environment.

We are looking for highly motivated researchers to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain. The projects are conducted in collaboration with UC Berkeley.

Two postdoctoral research positions and three PhD positions are available at the Sussex Centre for Consciousness Science (SCCS). The postdoc positions are funded for 2 years with a possible extension for another 2, starting as soon as suitable candidates are identified. The PhD positions are fully funded for 3.5 years, starting in September 2024, with an earlier start being negotiable. The ERC-funded positions will focus on computational (neuro)phenomenology, exploring different applications of a predictive processing view of conscious perception. One of the ERC PhD positions will specifically focus on perceptual diversity, analyzing and extending the Perception Census project. The SCCS PhD position is flexible regarding project focus and supervisor, inviting proposals with a computational/informatics element.

The project will be examining sensory processing in infants born prematurely and later trajectories of neurodevelopment, using a variety of neurocognitive methods. Infants were recruited as neonates and the postholder will be leading and conducting the follow up visits (when the infant is 18 months). The post would suit someone who is interested in infant development, neurodevelopmental conditions and has experience with infant/toddler EEG and eye tracking.

We have an open position for a postdoctoral researcher with experience in brain-computer interfacing and artificial intelligence to further advance our new class of Brain-Artificial Intelligence (BAI) interfaces. A central part of your research would be to further develop our BAI for single-unit data recorded in language areas of a post-stroke aphasia patient, a project we carry out in close collaboration with the Translational NeuroTechnology Lab at TUM, headed by Simon Jacob.

The Georgetown University Neuroscience of Language Training Program is seeking outstanding postdoctoral fellows who wish to become the future leaders of our field. We aim to develop well-rounded scientists who have a broad perspective on basic and clinical neuroscience of language research, along with the skills and track-record to succeed in their chosen career path. We offer a rich training environment in the nation’s capital where fellows conduct innovative research under the guidance of 18 faculty members studying basic and clinical neuroscience of language, along with sensory, motor, and cognitive systems as they pertain to language and communication. Fellows can work with a single faculty member or across multiple labs, including partner labs at Children’s National Hospital and the George Washington University. Fellows can also participate in clinical experiences, community engagement activities, professional development training, journal clubs, and seminars to enrich their training. Appointments are funded at NIH NRSA stipend rates for two years, assuming fellows remain in good standing after the first year. Fellows also receive additional funds for training-related expenses, such as workshops, courses, conference travel, computers, peripherals, etc.

Applications are invited for three-year PhD studentships at the University of Plymouth, UK. The studentships will start on Wednesday 1 October 2025. A list of projects can be found below. It is essential that candidates discuss their proposal/plans with their intended supervisor(s), prior to writing their proposal and submitting an application. The school can only consider PhD research proposals that have the support of a supervisor. The projects include: 1. AI-Based Analysis of Voice Biomarkers in Neurodevelopmental Disorders, 2. Virtual Reality Training for Spatial Familiarity with Autistic Individuals, 3. Enhancing Social Interaction for Autistic Individuals through Anthropomorphic Augmented Reality, 4. Examining Cohesion and Authoritarianism through Synchronised VR Interactions, 5. Pure Fantasy: Harnessing VR to Explore and Enhance the Ideal Self in Autistic Individuals, 6. Board games for autistic wellbeing.

The goal of this PhD is to explore a minimal model of decision making using a simulated agent in a contiguous environment (T-Maze like). The goal for the agent is to learn to alternate between left and right, independently of the geometry of the maze, even though topology remains the same. This will be done using an echo state network of limited size in order to be able to perform a thorough analysis of its dynamics and representations from three different perspectives (sensory-motor space, external behavior and neural activity). The goal is to find the conditions for the emergence of concepts such as left and right using a manifold-based approach and to prove for their existence independently an external observer.

At Chandar Lab and Mila, the Quebec AI Institute, we have four open postdoctoral positions on the following topics: 1. Postdoc position on large language models (LLMs) - Topics of interest include but not limited to better pre-training methods, better fine-tuning methods, bias and fairness, interpretability, safety and alignment, continual pre-training. 2. Postdoc position on foundation models for biological data - Topics of interest include but not limited to foundation models (both encoder and decoder models) for proteins, small molecules, and genomics data, multi-modal foundation models for biological data, 3d generative modelling, drug discovery. For this position, we are looking for a candidate with strong ML/LLM/Transformers/Foundation Models background. If you do not have the biological domain background, but are interested in exploring AI for science, this is a perfect position for you. In this position, you will be working closely with our pharmaceutical partners who are experts in biology. 3. Postdoc position on foundation models for time series data - Topics of interest includes but not limited to better sequential architectures, state space models, recurrent neural networks, attention-free architectures, etc. 4. Postdoc position on foundation models for Astrophysics - The topics of interest includes but not limited to recurrent inference machines, Transformers, diffusion models for radio images in Astrophysics.

The Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago seeks outstanding applicants for multiple postdoctoral positions in computational and theoretical neuroscience. We especially welcome applicants who develop mathematical approaches, computational models, and machine learning methods to study the brain at the circuits, systems, or cognitive levels. The current faculty members of the Grossman Center to work with are: Brent Doiron’s lab investigates how the cellular and synaptic circuitry of neuronal circuits supports the complex dynamics and computations that are routinely observed in the brain. Jorge Jaramillo’s lab investigates how subcortical structures interact with cortical circuits to subserve cognitive processes such as memory, attention, and decision making. Ramon Nogueira’s lab investigates the geometry of representations as the computational support of cognitive processes like abstraction in noisy artificial and biological neural networks. Marcella Noorman’s lab investigates how properties of synapses, neurons, and circuits shape the neural dynamics that enable flexible and efficient computation. Samuel Muscinelli’s lab studies how the anatomy of brain circuits both governs learning and adapts to it. We combine analytical theory, machine learning, and data analysis, in close collaboration with experimentalists. Appointees will have access to state-of-the-art facilities and multiple opportunities for collaboration with exceptional experimental labs within the Neuroscience Institute, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and UChicago’s Data Science Institute. The Neuroscience Institute is currently engaged in a major expansion that includes the incorporation of several new faculty members in the next few years.

We are seeking a PostDoc with a quantitative background who has finished (or about to finish) a doctoral degree in a quantitative field preferably but not limited to physics or engineering. The candidate should show enthusiasm for analysing large scale data sets that include but not limited to: behavioural, neural and physiological data. Experience with machine learning techniques and animal tracking software programs is preferred but not required. The researcher will be based in the integrative biophysics group at the University of Konstanz and Max Planck Institute of Animal Behavior, located in Konstanz, Germany. The Postdoc will be working as part of a recently funded Human Sciences Frontiers Program (HSFP) research grant ‘”Neurometabolic mechanisms underlying social foraging” in collaboration with the experimental groups of Robert Froemke (New York University) and Jee Hyun Choi (Korean Institute of Science and Technology). The project aims to understand neuro-metabolic mechanisms underlying social foraging. The PostDoc will have the opportunity to travel to the experimental collaborators in New York and Seoul. The Integrative Biophysics group at the CASCB led by Dr. Ahmed El Hady is focused on theoretical and computational understanding of mechanisms underlying foraging. The postdoc position will be embedded within the highly collaborative environment of the cluster for advanced study of collective behavior at the University of Konstanz.

We are hiring a Postdoctoral Research Associate at the Chair of Cognitive Computational Neuroscience, TU Dresden (Germany). The position is part of a DFG-funded project on neurocomputational mechanisms of decision-making through forward planning and state abstraction. Project highlights include modeling human learning and decision-making using probabilistic approaches, analyzing behavioral and fMRI data, collaborative work with experimentalists and theorists, and the opportunity to design and run experiments.

The Mnemosyne team of the Inria centre of the University of Bordeaux (France) is looking for a talented postdoctoral fellow with confirmed competences in the domain of Machine Learning for the development of a modeling framework of Metacognition. Metacognition is the cognitive process by which, instead of just learning to associate a response or a behavior with a situation, animals (and mainly primates) monitor the functioning (and particularly errors) of simple cognitive processes, learn to inhibit automatic responses and promote instead contextually appropriate behavioral rules. Better understanding and modeling this process is important for several reasons. In cognitive neuroscience, it paves the way to exploring higher cognitive functions like reasoning, imagination and other kinds of deliberation-based thoughts. In Artificial Intelligence, it stands on the same grounds as Generative AI and proposes different processes and algorithms that might remedy several weaknesses of GenAI and suggest innovative brain-inspired extensions. Located in Bordeaux (France), the role of the postdoctoral fellow to be recruited is to participate to a research program, under the following axes: Axis 1: Specification of Metacognition and its main computational mechanisms: Metacognition is generally described through three main mechanisms: (i) the possibility to monitor cues indicating difficulties in the process of problem solving (errors or conflicts between resources), in order to inhibit elementary default responses, (ii) working memory to keep in sustained activity the different aspects to be integrated (goals and subgoals, predictions, constraints) and (iii) cognitive flexibility corresponding to new goals and contextual rules that can be learned and integrated in the process of problem solving. Existing models (including from our team) indicate possible correspondence with cerebral circuitries and adaptive operations. Nevertheless, they are many and split these general mechanisms in different pieces which are not always consistent and may differ under several aspects. A major contribution will be to carry out a thorough analysis of these elements, to propose a synthesis associating both a precise description of the mechanisms and a map of their functional dependencies. Axis 2: Definition of relevant tasks in the domain of visual reasoning: Although many standard tasks have been defined and shared for simple sensorimotor control, it is not yet the case for cognitive control, generally corresponding to much more complex behaviors. A variety of tasks have been proposed in models evoked above but they differently integrate fundamental constituents such as hierarchical and temporal dependencies. In a similar view of standardization as in the axis above, the goal will be consequently to enumerate properties that have to be assessed when developing such metacognitive models and propose or design corresponding tasks. Subsequently, the postdoctoral fellow will work on integrating the insights from Axis 1 and task definitions in this Axis, with an architecture that integrates selected mechanisms from the different frameworks, particularly under the perspective of extending and evaluating models proposed in our team with novel properties. Axis 3: Organization of an international network of collaboration on the topic: We have already begun to identify and contact international (mainly European) teams working on the topic and willing to contribute to the elaboration of such a roadmap, toward more ambitious international projects. A corresponding goal will be to interact with these partners and to help with the preparation of such projects. This postdoc position is proposed for 18 to 24 months, preferably starting on November 1st, 2025 and will be located in the Mnemosyne team, in Bordeaux, France.

The Senn lab offers a Postdoc or PhD position on a computational model of cortical self-attention (supported by EBRAINS 2.0). Based on our current work and a collaboration with The Virtual Brain (Marmaduke Woodman), we seek for an implementation of neuronal self-attention mechanisms in thalamo-cortical circuits. The model is inspired by transformer-type architectures, but is consistent with experimentally observed cortical connectivity patterns. It shall be trained on cognitive tasks while being constrained to human cortical recordings. The Senn lab also offers a Postdoc or PhD position on the Neuronal Least-Action principle and its extension to long-term temporal processing and spikes (supported by the SNSF). The framework offers a rigorous description of the neuronal dynamics in cortical networks together with gradient-based synaptic learning rules. It will be applied to integrate-and-fire neurons with multiple intrinsic time constants. The project links to implementations in spike-based neuromorphic hardware.