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.

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.

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.

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 School of Applied Mathematics at Fundação Getulio Vargas (FGV EMAp) in Rio de Janeiro, Brazil, invites applications for one open-rank faculty position in Data Science to strengthen and complement our existing research activity in this area. We are looking for established researchers (associate/full professor) or outstanding young researchers (assistant professor) who have demonstrated research and teaching expertise in Data Science. We will prioritize applicants whose research focuses on natural language processing, computer vision, reinforcement learning, network science and data mining, but we also welcome applications from other fields in Data Science. The successful candidate is expected to develop an externally funded research programme, publish in high-impact venues, supervise research (postgraduate) students, teach at both undergraduate and graduate levels, and provide service to the department and institution. In general, teaching duties consist of two courses per year, one at Undergraduate and one at Graduate level. Peer-reviewed external funding is expected to be obtained and sustained. Industrial partnerships are also strongly encouraged.

Applications are invited for a Postdoctoral Research Associate post in the Cardiff University School of Computer Science & Informatics, to work on the InteGraL project (“Interpretable Graph-Based Machine Learning”). This Leverhulme Trust funded project is focused on developing alternatives to Graph Neural Networks (GNNs). Its central aim will be to introduce methods that are interpretable by design, while at the same time being more robust than GNNs and generalising better to problem instances that are out of the training set distribution. The models will be applied to problems in Natural Language Processing and Computational Chemistry, among others. More details about the post and instructions on how to apply are available at https://www.jobs.ac.uk/job/DMZ697/research-associate

The Mackelab (Prof. Jakob Macke, University Tübingen) is looking for PhD, Postdoc and Scientific Programmer applicants interested in working with us on using deep learning to build, optimize and study mechanistic models of neural computations! In a first project, funded by the ERC Grant DeepCoMechTome, we want to make use of connectomic reconstructions of the fruit fly to build large-scale simulations of the fly brain that can explain visually driven behavior—see, e.g., our prior work with Srinivas Turaga’s group, described in Lappalainen et al., Nature, 2024. In a second project, funded by the DFG through the CRC Robust Vision, we want to use differentiable simulators of biophysical models (Deistler et al., 2024) to build data-driven models of visual processing in the retina. We are open to candidates who are more interested in neurobiological questions, as well as to ones more interested in machine learning aspects (e.g. training large-scale mechanistic neural networks, learning efficient emulators, coding frameworks for collaborative modelling, automated model discovery for mechanistic models, …) of these projects.

The Center for Theoretical and Computational Neuroscience (CTCN) at Washington University in St Louis invites applications from outstanding Postdoctoral Fellows to work at the interface between theoretical and experimental neuroscience labs at WashU. The CTCN is a joint initiative between the Schools of Medicine, Engineering, and Arts and Sciences, and provides a hub for neuroscientists to collaborate with mathematicians, physicists and engineers to find creative solutions to some of the most difficult problems currently facing neuroscience and artificial intelligence. Each CTCN Postdoctoral Fellow is based in at least two labs, but also has the opportunity to seek out new collaborations which help build new connections within the WashU community. We are looking for people with drive, independence and outstanding prior achievement, who are committed to leveraging interdisciplinary collaboration to drive forward the field of theoretical and computational neuroscience. Washington University in St Louis is ranked in the top 10 worldwide for Neuroscience and Behavior. Salary for CTCN Fellows is significantly above standard NIH postdoc rates, and funds for conference travel are included. In addition, WashU offers excellent benefits and comprehensive access to career development, professional and personal support. The St Louis metropolitan area has a population of almost 3M and is rich in culture, green spaces and thriving music and arts scenes, with a highly accessible cost of living.

The CSNG Lab at the Faculty of Mathematics and Physics at the Charles University is seeking a highly motivated Postdoctoral Researcher to join our team to work on a digital twin model of the visual system. Funded by the JUNIOR Post-Doc Fund, this position offers an exciting opportunity to conduct cutting-edge research at the intersection of systems neuroscience, computational modeling, and AI. The project involves developing novel modular, multi-layer recurrent neural network (RNN) architectures that directly mirror the architecture of the primary visual cortex. Our models will establish a one-to-one mapping between individual neurons at different stages of the visual pathway and their artificial counterparts. They will explicitly incorporate functionally specific lateral recurrent interactions, excitatory and inhibitory neuronal classes, complex single-neuron transfer functions with adaptive mechanisms, synaptic depression, and others. We will first train our new RNNs on synthetic data generated by a state-of-the-art biologically realistic recurrent spiking model of the primary visual cortex developed in our group. After establishing the proof-of-concept on the synthetic data, we will translate our models to publicly available mouse and macaque data, as well as additional data from our experimental collaborators.