The López-Schier laboratory is looking for PhD candidates to join a multidisciplinary research project that combines experimental and computational neuroscience. The aim of the project is to understand the neuronal bases of spatial navigation. The project is fully funded and part of a consortium of experimental and theoretical neuroscientists in Germany, France and the USA. We are looking for outstanding, highly motivated and ambitious candidates with a solid background in physics, engineering, computer science, or theoretical neuroscience, and a genuine interest in animal behaviour. The positions are fully funded with ideally start in March-June 2021. You will join a multidisciplinary team at the Helmholtz Zentrum in Neuherberg-Munich, Germany. A good command of the English language is necessary. Other requirements are computer programming skills, and good understanding machine learning and machine vision. The Helmholtz Zentrum München is world-renowned for its fundamental research and is among the top research institutions in the world. Munich is cosmopolitan city with a lively lifestyle and outstanding outdoors. Candidates must send their application including a brief letter of interest, a complete CV, as well as contact information of two or three academic references to Dr. Hernan Lopez-Schier

Dr. Jessica Ausborn’s group at Drexel University College of Medicine, in the Department of Neurobiology & Anatomy has a postdoctoral position available for an exciting new research project involving computational models of sensorimotor integration based on neural and behavior data in Drosophila. The interdisciplinary collaboration with the experimental group of Dr. Katie von Reyn (School of Biomedical Engineering) will involve a variety of computational techniques including the development of biophysically detailed and more abstract mathematical models together with machine learning and data science techniques to identify and describe the algorithms computed in neuronal pathways that perform sensorimotor transformations. The Ausborn laboratory is part of an interdisciplinary group of Drexel’s Neuroengineering program that includes computational and experimental investigators. This collaborative, interdisciplinary environment enables us to probe biological systems in a way that would not be possible with either an exclusively experimental or computational approach. Applicants should forward a cover letter, curriculum vitae, statement of research interests, and contact information of three references to Jessica Ausborn (ja696@drexel.edu). Salary will be commensurate with experience based on NIH guidelines.

The laboratories of Dr. Richard Mooney (https://www.neuro.duke.edu/mooney-lab) and Dr. John Pearson (http://pearsonlab.github.io) at Duke University are seeking two (2) postdoctoral scholars in conjunction with an NIH BRAIN Initiative-funded project investigating the contributions of basal ganglia to vocal motor exploration and reinforcement learning. Candidates will combine state of the art viral, electrophysiological, imaging, and computational methods and work as part of a multi-institution team that also includes Dr. Carlos Lois, in the Division of Biology and Biological Engineering at CalTech, and Dr. Tim Gardner, in the Phil and Penny Knight Campus for Accelerating Scientific Impact at the University of Oregon. The first postdoc, appointed in the Department of Neurobiology (https://careers.duke.edu/job/Durham-POSTDOCTORAL-ASSOCIATE-NC-27710/681792500/), will use behavioral, optogenetic, electrophysiological and optical imaging methods to explore how cortico-basal ganglia circuits contribute to vocal exploration and learning. Previous experience with imaging and electrophysiological methods is desirable, and experience using viral gene transfer methods to monitor and manipulate neural activity will be especially helpful. Candidates with strong quantitative skills and an interest in developing or improving computational skills are especially desired. This postdoc will work closely with a related postdoc hire in the Department of Biostatistics & Bioinformatics, as well as team members across all institutions. The second postdoc, appointed in the Department of Biostatistics & Bioinformatics (https://careers.duke.edu/job/Durham-POSTDOCTORAL-ASSOCIATE-NC-27710/681799400/), will perform computational modeling of reinforcement learning in the birdsong system, including development of new statistical machine learning methods for the analysis of song, electrophysiology, and calcium imaging data. The postdoc will work closely with experimentalists to design studies, analyze data, and refine hypotheses. Candidates should hold a PhD in a quantitative discipline such as computational neuroscience, physics, statistics, or computer science. Previous experience in neurobiology is a plus but not required. This postdoc will work closely with a related postdoc hire in the Department of Neurobiology, as well as team members across the other institutions.

A postdoctoral position in computational neuroscience is available in the lab of Christopher Rozell at the Georgia Institute of Technology (Atlanta, GA). This BRAIN Initiative research project seeks to advance the field of closed-loop computational neuroscience by pioneering the use of real-time feedback stimulation during experiments to decouple recurrently connected circuit elements and make stronger causal inferences about circuit interactions. This position will have a broad opportunity to develop models and algorithms that are implemented in novel experiments using closed-loop optogenetic stimulation. We aim to provide both new scientific insight about computation in neural circuits (especially sensory coding in the thalamo-cortical circuit) as well as new approaches and algorithmic tools for the community to use in novel electrophysiology experiments. This position will work as part of a team and in close collaboration with the experimental lab of Garrett Stanley (also at Georgia Tech), and it is expected that the computational and algorithmic approaches will be implemented experimentally through close partnership with experimentalists in the Stanley Laboratory. Applicants should hold a PhD in a related discipline with a strong record of research impact, quantitative thinking and collaborative work. Experience in computational neuroscience, machine learning, feedback control, and causal inference is all advantageous. The lab is committed to providing a diverse and inclusive environment for all scholars, and applications are especially encouraged from all underrepresented groups. Additionally, the lab is committed to the professional development of the members, making it valuable preparation for people who are interested in academic, industrial or entrepreneurial careers. The position has no mandatory teaching or administrative duties. Excellent (written and oral) communication skills in English are required. This particular project is part of the Collaborative Research in Computational Neuroscience program (CRCNS), providing access to a community of researchers across the country who are focused on similar types of collaborations between computational and experimental labs. Georgia Tech's campus in the heart of midtown Atlanta, which has a thriving and collaborative neuroscience community that has a particular emphasis on computational and systems neuroscience. Atlanta is also one of the fastest-growing metropolitan areas in the United States, boasting a wide range of opportunities for recreation and culture. Georgia Tech has competitive benefits (including comprehensive medical insurance) and is an equal opportunity employer. The position would ideally start as soon as possible (spring 2021). The appointment is initially for 12 months with the expectation of renewal. Compensation will be commensurate with relevant experience. Candidates should send a CV, a statement of research experience and interests, expected date of availability, and the contact information for three references to crozell@gatech.edu with the subject line "CRCNS postdoc". Application review will proceed until the position is filled and should be received by December 1 for full consideration.

The Engel lab in the Department of Neuroscience at Cold Spring Harbor Laboratory invites applications from highly motivated candidates for a postdoctoral position working on the cutting-edge research in computational neuroscience. We are looking for theoretical/computational scientists to work at the exciting interface of systems neuroscience, machine learning, and statistical physics, in close collaboration with experimentalists. The postdoctoral scientist is expected to exhibit resourcefulness and independence, developing computational models of large-scale neural activity recordings with the goal to elucidate neural circuit mechanisms underlying cognitive functions. Details: https://cshl.peopleadmin.com/postings/15840

The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The selected candidate will expand the computational modeling framework Cobel-RL and use it to study how episodic memory might be used to learn to navigate.

The #Imbizo2024 is a southern hemisphere summer school aiming to promote computational neuroscience in Africa. It will bring together international and local students under the tutelage of the world's leading experts in the field. This four-week summer school aims to teach central ideas, methods, and practices of modern computational neuroscience through a combination of lectures and hands-on project work. Mornings will be devoted to lectures on topics across the breadth of computational neuroscience, including experimental underpinnings and machine learning analogues. The rest of the day will be spent working on research projects under the close supervision of expert tutors and faculty. Individual research projects will focus on the modelling of neurons, neural systems, behaviour, the analysis of state-of-the-art neural data, and the development of theories to explain experimental observations. It also includes a week focused on neuroscience-inspired machine learning.

Postdoctoral scientist positions are available at the Nathan Kline Institute (NKI) for Psychiatric Research to work on computational neuroscience research funded by NIH and DoD grants. Applicants should have a PhD in computational neuroscience (or a related field), strong background in multiscale modeling using NEURON/NetPyNE, Python software development, neural/electrophysiology data analysis, machine learning, and writing/presenting research.

IIT welcomes applicants with an outstanding track-record in Computational Neuroscience. Appropriate research areas include computational and modelling approaches for understanding the function of the nervous system. Investigators with expertise in mathematics, physics, statistics, and machine learning for neuroscience are also encouraged to apply. The position can be either tenured or tenure-track, depending on seniority and expertise. If tenure-track, the position is for an initial period of 5 years with renewal depending on evaluation. We provide generous support for salary, start-up budget, and annual running costs.

The position is part of the Collaborative Research Center “Extinction Learning” (SFB 1280) and studies the principles underlying spatial learning and its extinction with reinforcement learning models. A particular focus is the role of episodic-like memory in learning and extinction processes. The research group is highly dynamic and uses diverse computational modeling approaches including biological neural networks, cognitive modeling, and machine learning to investigate learning and memory in humans and animals.

The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The group is actively seeking a talented graduate student to join the team, who will expand the computational modeling framework Cobel-RL (https://doi.org/10.3389/fninf.2023.1134405) and use it to study how episodic memory might be used to learn to navigate.

The postdoctoral position is within the Computational Systems Neuroscience Group (CSNG) at Charles University, Prague, focusing on computational neuroscience and neuro-prosthetic system design. The project goals include developing a large-scale model of electrical stimulation in the primary visual cortex for neuro-prosthetic vision restoration, creating and refining models of the primary visual cortex and its electrical stimulation, simulating the impact of external stimulation on cortical activity, developing novel machine learning methods to link simulated cortical activity to expected visual perceptions, and developing stimulation protocols for neuro-prosthetic systems. This project is undertaken as a part of a larger consortium of Czech experimental and theoretical neuroscience teams.

A postdoctoral position within the Computational Systems Neuroscience Group (CSNG) at Charles University, Prague, focusing on computational neuroscience and neuro-prosthetic system design. The group explores the intricacies of the visual system, sensory coding, and neuro-prosthetic solutions using computational approaches such as large-scale biologically detailed spiking network models, firing-rate models of development, and modern machine learning techniques. The team is dedicated to understanding visual perception and its restoration via neuro-prosthetic devices. Multiple project topics are available and can be adjusted to the interest and background of the applicant, including modeling electrical stimulation in a spiking model of the primary visual cortex, deep-neural networks in visual neuroscience, study of cortical dynamics in the visual cortex, and biologically detailed spiking large-scale models of early visual cortical pathway from Retina to V4.

A fully funded PhD position in Computational Neuroscience is available at the University of Cyprus in collaboration with the University of Surrey (UK), titled “Brain Neuronal Networks Development via Multiscale Agent-based Modelling”. The project aims to demonstrate an innovative computational approach to model and emulate biological neural networks (NNs) by modelling NN development from a single precursor cell. The approach is inspired by the biological brain, using developmental rules encoded in a gene-type manner to reproduce challenging neural complexities. The project will use data from experimental studies and synthetic, simulated data to inform the computational modelling, aiming to create realistic NNs structurally and functionally. Innovative machine learning techniques will be employed to match the in-silico NNs with specific organisms, starting with synthetically generated NNs and increasing biological correspondence. The project will utilize the agent-based modelling software BioDynaMo, an open-source software actively developed for almost a decade.

The postdoctoral positions are focused on investigating how networks of neurons in the cerebral cortex encode, process, and transmit information to generate behaviors such as sensation and decision-making. The research involves developing and using information-theoretic and machine learning methods to study population coding, as well as neural network models to individuate mechanisms for neural information processing and generation of functions. The laboratory has extensive international collaborations and offers a well-funded research environment with opportunities for advanced training and personal scientific growth.

A fully funded PhD position in Computational Neuroscience is available at the University of Cyprus in collaboration with the University of Surrey (UK), titled “Brain Neuronal Networks Development via Multiscale Agent-based Modelling”. The project aims to model and emulate biological neural networks (NNs) development from a single precursor cell using a computational approach. By leveraging developmental rules encoded in a gene-type manner, the project seeks to reproduce neural complexities found in nature. The computational modelling will utilize data from experimental studies and synthetic, simulated data to inform realistic NNs structurally and functionally. Innovative machine learning techniques will be employed to match in-silico NNs with specific organisms, starting with synthetically generated NNs and increasing biological correspondence iteratively. The project will use the agent-based modelling software BioDynaMo, an open-source software actively developed for almost a decade. This builds on previous work of the supervisory team, including the simulation of a spatially embedded, functional, and biologically realistic neural network that self-organized from a single precursor cell.

Doctoral Position in Computational Neuroscience. Are you curious about how the human brain stores memories? Have you wondered how we manage to navigate through space? Our dynamic research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. Currently, we are actively seeking a talented graduate student to join our team, someone who will expand our computational modeling framework Cobel-Spike and use it to study how spiking neural networks can learn to navigate. This position is 65% at TV-L E13, starts as soon as possible, and is funded for 3 years.