SISSA cognitive neuroscience PhD

Up to 2 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2024. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience group (https://phdcns.sissa.it/) hosts 6 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language, tactile perception and learning, and neural computation. Our research is highly interdisciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply. The selection procedure is now open. The application deadline is 27 August 2024. Please apply here (https://www.sissa.it/bandi/ammissione-ai-corsi-di-philosophiae-doctor-posizioni-cofinanziate-dal-fondo-sociale-europeo), and see the admission procedure page (https://phdcns.sissa.it/admission-procedure) for more information. Note that the positions available for the Fall admission round are those funded by the "Fondo Sociale Europeo Plus", accessible through the first link above. Please contact the PhD Coordinator Mathew Diamond (diamond@sissa.it) and/or your prospective supervisor for more information and informal inquiries.

Eugenio Piasini

Up to 6 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2024. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience group (https://phdcns.sissa.it/) hosts 7 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language and reading, tactile perception and learning, and neural computation. Our research is highly interdisciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply. This year, one of the PhD scholarships is set aside for joint PhD projects across PhD programs within the Neuroscience department (https://www.sissa.it/research/neuroscience). The selection procedure is now open. The application deadline is 28 March 2024. To learn how to apply, please visit https://phdcns.sissa.it/admission-procedure . Please contact the PhD Coordinator Mathew Diamond (diamond@sissa.it) and/or your prospective supervisor for more information and informal inquiries.

Prof. KongFatt Wong-Lin

Postdoctoral Research Associate Position in Computational Neuroscience (Computational Modelling of Decision Making) Applications are invited for an externally funded Postdoctoral Research Associate position at the Intelligent Systems Research Centre (ISRC) in Ulster University, UK. The successful candidate will develop and apply computational modelling, and theoretical and analytical techniques to understand brain and behavioural data across primate species, and to apply biologically based neural network modelling to elucidate mechanisms underlying perceptual decision-making. The duration of the position is 24 months, from January 2024 till end of 2025.  The personnel will be based at the ISRC in Ulster University, working with Prof. KongFatt Wong-Lin and his team, while collaborating closely with international collaborators in the USA and the Republic of Ireland, namely, Prof. Michael Shadlen at Columbia University (USA), Prof. Stephan Bickel at Northwell-Hofstra School of Medicine (USA), Prof. Redmond O'Connell at Trinity College Dublin (Ireland), Prof. Simon Kelly at University College Dublin (Ireland), and Prof. S. Shushruth at University of Pittsburgh (USA).  The ISRC is dedicated to developing a bio-inspired computational basis for AI to power future cognitive technologies. This is achieved through understanding how the brain works at multiple levels, from cells to cognition and apply that understanding to create models and technologies that solve complex issues that face people and society. All applicants should hold a degree in in Computational Neuroscience, Computational Biology, Neuroscience, Computing, Engineering, Mathematics, Data Science, Physical Sciences, Biology, or a cognate area.  Apply online: https://my.corehr.com/pls/coreportal_ulsp/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_applicant_no=&p_recruitment_id=023762&p_process_type=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y Closing date for receipt of completed applications: 8th November 2023.  Job Ref: 023762. For any informal enquiries regarding this position, please contact KongFatt Wong-Lin; email: k.wong-lin@ulster.ac.uk ; website: https://www.ulster.ac.uk/staff/k-wong-lin

SueYeon Chung, Center for Computational Neuroscience, Flatiron Institute

Flatiron Research Fellow (Postdoctoral Fellow), NeuroAI and Geometric Data Analysis Description Applications are invited for Flatiron Research Fellowships (FRF) in the NeuroAI and Geometric Data Analysis Group (SueYeon Chung, PI) at the Center for Computational Neuroscience at the Flatiron Institute of the Simons Foundation, whose focus is on understanding computation in the brain and artificial neural networks by: (1) analyzing geometries underlying neural or feature representations, embedding and transferring information, and (2) developing neural network models and learning rules guided by neuroscience. To do this, the group utilizes analytical methods from statistical physics, machine learning theory, and high-dimensional statistics and geometry.The CCN FRF program offers the opportunity for postdoctoral research in areas that have strong synergy with one or more of the existing research groups at CCN or other centers at the Flatiron Institute. In addition to carrying out an independent research program, Flatiron Research Fellows are expected to: disseminate their results through scientific presentations, publications, and software release, collaborate with other members of the CCN or Flatiron Institute, and participate in the scientific life of the CCN and Flatiron Institute by attending seminars, colloquia, and group meetings. Flatiron Research Fellows may have the opportunity to organize workshops and to mentor graduate and undergraduate students. The mission of CCN is to develop theories, models, and computational methods that deepen our knowledge of brain function — both in health and in disease. CCN takes a “systems" neuroscience approach, building models that are motivated by fundamental principles, that are constrained by properties of neural circuits and responses, and that provide insights into perception, cognition and behavior. This cross-disciplinary approach not only leads to the design of new model-driven scientific experiments, but also encapsulates current functional descriptions of the brain that can spur the development of new engineered computational systems, especially in the realm of machine learning. CCN’s current research groups include computational vision (Eero Simoncelli, PI), neural circuits and algorithms (Dmitri ‘Mitya’ Chklovskii, PI), neuroAI and geometric data analysis (SueYeon Chung, PI), and statistical analysis of neural data (Alex Williams, PI), and is planning to expand the number of research groups in the near term. Interested candidates should review the CCN public website for specific information on CCN’s research areas. Applicants who are interested in a joint appointment between two CCN research groups should submit the same application to both groups, noting the dual application in their research statement. Please note that Alex William’s statistical analysis of neural data group is not recruiting at CCN in 2023. FRF positions are two-year appointments and are generally renewed for a third year, contingent on performance. FRF receive a research budget and have access to the Flatiron Institute’s powerful scientific computing resources. FRF may be eligible for subsidized housing within walking distance of the CCN. Review of applications for positions starting between July and October 2024 will begin in November 2023. For more information about life at the Flatiron Institute, visit https://www.simonsfoundation.org/flatiron/careers.

Cognitive Neuroscience PhD program @ SISSA

Up to 6 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2023. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience Department (https://phdcns.sissa.it/) hosts 7 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language and reading, tactile perception and learning, and neural computation. The Department is highly interdisciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply. The selection procedure is now open. The first application deadline is 31 March 2023. To learn how to apply, please visit https://phdcns.sissa.it/admission-procedure. Please contact the PhD Coordinator Mathew Diamond (diamond@sissa.it) and/or your prospective supervisor for more information and informal inquiries.

Sainsbury Wellcome Centre, UCL

Applications are now open for the 2023 intake to our PhD Programme at the Sainsbury Wellcome Centre at University College London (UCL). This fully-funded 4-year programme offers students: • a comprehensive introduction to theoretical and systems neuroscience • intensive training in experimental techniques, including imaging, physiology, molecular, and behavioural methods in systems neuroscience • a supportive and collaborative environment with teaching by SWC faculty together with colleagues at the Gatsby Computational Neuroscience Unit and other affiliated institutions Based in central London, with the highest concentration of neuroscience research in the world, SWC students are fully funded and receive an annual stipend of £24,278, as well as funds to attend international courses or meetings. We also cover the cost of tuition fees for both home and international students. The SWC PhD programme is an opportunity to receive world-class training as a neuroscientist and launch an exciting career in academia or industry. Apply to join our pool of exceptional students from around the globe. More information on the SWC PhD programme, and details on how to apply, can be found on the website: https://www.sainsburywellcome.org/web/content/neuroscience-phd-programme If you have any queries about the SWC PhD programme, or the application process, please contact us: SWC-PhDprogramme@ucl.ac.uk.

Dr. Gabriele Scheler

We are offering a research stipend to investigate theories of memorization in neural plasticity. The focus is a critical evaluation of the role of LTP/LTD and synaptic plasticity in memory. This position is virtual and could be done part-time, or full-time for three months. The ideal candidate should have solid knowledge of neurobiology, especially plasticity mechanisms, excellent communication skills, interest and enthusiasm for next-generation neural theories, a good understanding of computation and mathematics. Programming skills are not required for this position. Detailed knowledge of one area of neural plasticity, such as synapses, intracellular pathways or genetics, is expected. Further information available on request.

Mario Dipoppa

The selected candidates will be working on questions addressing how brain computations emerge from the dynamics of the underlying neural circuits and how the neural code is shaped by computational needs and biological constraints of the brain. To tackle these questions, we employ a multidisciplinary approach that combines state-of-the-art modeling techniques and theoretical frameworks, which include but are not limited to data-driven circuit models, biologically realistic deep learning models, abstract neural network models, machine learning methods, and analysis of the neural code.

Yashar Ahmadian

The postdoc will work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), and Zoe Kourtzi at the Psychology Department, both at the University of Cambridge. The project investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptive changes in the balance of cortical excitation and inhibition resulting from perceptual learning. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

Professor Geoffrey J Goodhill

The Department of Neuroscience at Washington University School of Medicine is currently recruiting investigators with the passion to create knowledge, pursue bold visions, and challenge canonical thinking as we expand into our new 600,000 sq ft purpose-built neurosciences research building. We are now seeking a tenure-track investigator at the level of Assistant Professor to develop an innovative research program in Theoretical/Computational Neuroscience. The successful candidates will join a thriving theoretical/computational neuroscience community at Washington University, including the new Center for Theoretical and Computational Neuroscience. In addition, the Department also has world-class research strengths in systems, circuits and behavior, cellular and molecular neuroscience using a variety of animal models including worms, flies, zebrafish, rodents and non-human primates. We are particularly interested in outstanding researchers who are both creative and collaborative.

Haim Sompolinsky, Kenneth Blum

The Swartz Program at Harvard University seeks applicants for a postdoctoral fellow in theoretical and computational neuroscience. Based on a grant from the Swartz Foundation, a Swartz postdoctoral fellowship is available at Harvard University with a start date in the summer or fall of 2024. Postdocs join a vibrant group of theoretical and experimental neuroscientists plus theorists in allied fields at Harvard’s Center for Brain Science. The Center for Brain Science includes faculty doing research on a wide variety of topics, including neural mechanisms of rodent learning, decision-making, and sex-specific and social behaviors; reinforcement learning in rodents and humans; human motor control; behavioral and fMRI studies of human cognition; circuit mechanisms of learning and behavior in worms, larval flies, and larval zebrafish; circuit mechanisms of individual differences in flies and humans; rodent and fly olfaction; inhibitory circuit development; retinal circuits; and large-scale reconstruction of detailed brain circuitry.

The Brain Prize winners' webinar

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Flexible motor sequence generation by thalamic control of cortical dynamics through low-rank connectivity perturbations

One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales to efficiently shape structured behaviors. I will present a model that clarifies how these complex computations could be performed in the mammalian brain, with an emphasis on the learning of an extendable library of autonomous motor motifs and the flexible stringing of these motifs in motor sequences. To build this model, we took advantage of the fact that the anatomy of the circuits involved is well known. Our results show how these architectural constraints lead to a principled understanding of how strategically positioned plastic connections located within motif-specific thalamocortical loops can interact with cortical dynamics that are shared across motifs to create an efficient form of modularity. This occurs because the cortical dynamics can be controlled by the activation of as few as one thalamic unit, which induces a low-rank perturbation of the cortical connectivity, and significantly expands the range of outputs that the network can produce. Finally, our results show that transitions between any motifs can be facilitated by a specific thalamic population that participates in preparing cortex for the execution of the next motif. Taken together, our model sheds light on the neural network mechanisms that can generate flexible sequencing of varied motor motifs.

Parametric control of flexible timing through low-dimensional neural manifolds

Biological brains possess an exceptional ability to infer relevant behavioral responses to a wide range of stimuli from only a few examples. This capacity to generalize beyond the training set has been proven particularly challenging to realize in artificial systems. How neural processes enable this capacity to extrapolate to novel stimuli is a fundamental open question. A prominent but underexplored hypothesis suggests that generalization is facilitated by a low-dimensional organization of collective neural activity, yet evidence for the underlying neural mechanisms remains wanting. Combining network modeling, theory and neural data analysis, we tested this hypothesis in the framework of flexible timing tasks, which rely on the interplay between inputs and recurrent dynamics. We first trained recurrent neural networks on a set of timing tasks while minimizing the dimensionality of neural activity by imposing low-rank constraints on the connectivity, and compared the performance and generalization capabilities with networks trained without any constraint. We then examined the trained networks, characterized the dynamical mechanisms underlying the computations, and verified their predictions in neural recordings. Our key finding is that low-dimensional dynamics strongly increases the ability to extrapolate to inputs outside of the range used in training. Critically, this capacity to generalize relies on controlling the low-dimensional dynamics by a parametric contextual input. We found that this parametric control of extrapolation was based on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds in activity space while preserving their geometry. Comparisons with neural recordings in the dorsomedial frontal cortex of macaque monkeys performing flexible timing tasks confirmed the geometric and dynamical signatures of this mechanism. Altogether, our results tie together a number of previous experimental findings and suggest that the low-dimensional organization of neural dynamics plays a central role in generalizable behaviors.

Theory of recurrent neural networks – from parameter inference to intrinsic timescales in spiking networks

Neural network models – analysis of their spontaneous activity and their response to single-neuron stimulation