November 2021 - Comp-neuro - lists.cnsorg.org

Postdoctoral position to study circuit mechanisms of adaptive learning at Dartmouth College
by Alireza Soltani 03 Nov '21

03 Nov '21

The Computational and Cognitive Neuroscience Lab (CCNL) at Dartmouth College has an opening for an NIH-funded postdoctoral position to work on an exciting project on circuit mechanisms of reward-based learning under uncertainty. The project involves computational modeling and data analyses (behavioral and calcium imaging) in close collaboration with Alicia Izquierdo's lab at UCLA. Initial appointment is for one year with opportunity for renewal. The position is open to start immediately, but applications will be considered until the position is filled. A few recent relevant papers include: 1. Soltani A, Izquierdo A (2019). Adaptive Learning under Expected and Unexpected Uncertainty. Nature Reviews Neuroscience, 20(10): 635–644 2. Farashahi S, Donahue C, Hayden B, Lee D, Soltani A (2019). Flexible Combination of Reward Information across Primates. Nature Human Behaviour, 3: 1215–1224 3. Stolyarova A, Rakhshan M, Peters MA, Lau H, Soltani A, Izquierdo A (2019). Contributions of Anterior Cingulate Cortex and Basolateral Amygdala in Decision Confidence and Learning under Uncertainty. Nature Communications, 10:4704 4. Spitmaan MM, Seo H, Lee D, Soltani A (2020). Multiple Timescales of Neural Dynamics and Integration of Task-relevant Signals across Cortex. Proceedings of the National Academy of Sciences, 117 (36), 22522-22531 5. Trepka E, Spitmaan MM, Bari BA, Costa VD, Cohen JY, Soltani A (2021). Novel entropy-based metrics for predicting choice behavior based on local response to reward. Nature Communications (Accepted) [available on bioRxiv, 2021.05.20.445009v1] For more recent publications from the lab please visit http://ccnl.dartmouth.edu. Interested individuals should contact Alireza Soltani (soltani(a)dartmouth.edu) with their CV. Alireza Soltani, Ph.D. Associate Professor Department of Psychological and Brain Sciences Dartmouth College, NH Alireza.Soltani(a)Dartmouth.edu http://ccnl.dartmouth.edu Phone: 603-646-2998 Office: Moore 356

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BCI Un-Conference: Submission open
by Anja Meunier 02 Nov '21

02 Nov '21

Dear colleagues, registration and abstract submission for the 3rd BCI Un-Conference is now open [1]. Abstract submissions are text-only. The length and format of the submissions are up to the authors - use whatever format you consider best for attracting up-votes! Important dates: - Submission deadline: December 3rd, 2021 at 11:59 pm (CET) - Voting period: December 6th, 2021 - December 12th, 2021 at 11:59 pm (CET) - BCI-UC event: January 27th, 2022, from 03:00 pm to 09:00 pm (CET) We will invite the authors of the top-voted abstracts to present at the un-conference. The 3rd BCI-UC will feature keynotes by Cynthia Chestek (University of Michigan) [2] and Thorsten Zander (TU Brandenburg) [3]. BCI-UC is an online un-conference that provides rapid dissemination of novel research results in the BCI community. Participants can submit abstracts to apply for 20 minutes presentation slots. Abstracts are not reviewed by a program committee. Rather, all registered participants can vote on which of the submitted abstracts they would like to see presented at the un-conference. Because the BCI-UC does not publish conference proceedings, submitted abstracts can report novel as well as already published work. Registration and attendance are free of charge. Presentations of the 1st and 2nd BCI-UC can be re-watched at [4] and [5]. All presentations will be streamed via Crowdcast [6] and can be accessed free of charge. Join us in supporting this novel community-driven dissemination of research results! The BCI-UC committee: Moritz Grosse-Wentrup Anja Meunier Philipp Raggam Jiachen Xu Links: [1] https://bciunconference.univie.ac.at/register-vote/ [2] https://scholar.google.com/citations?user=36sxAZEAAAAJ&hl=de [3] https://scholar.google.com/citations?hl=en&user=0E49HxYAAAAJ [4] https://bciunconference.univie.ac.at/past-events/1st-bci-uc/ [5] https://bciunconference.univie.ac.at/past-events/2nd-bci-uc/ [6] https://www.crowdcast.io/

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Postdoc position in Computational and Systems Neuroscience at Research Center Jülich, Germany
by Martina Reske 02 Nov '21

02 Nov '21

The working group Statistical Neuroscience at INM-6 Computational and Systems Neuroscience<https://www.fz-juelich.de/inm/inm-6/EN/Home/home_node_INM6.html> at Research Center Juelich, Germany, led by Sonja Grün<https://www.fz-juelich.de/SharedDocs/Personen/INM/INM-6/EN/staff/Gruen_Sonj…> develops statistical methods for the analysis of experimental electrophysiological data from massively parallel electrophysiological recordings and leads the development of open source software tools for the analysis of activity data and validation of models on the population level. The department aims to fill a postdoctoral position that is responsible for the analysis of massively parallel spike and LFP data acquired in a collaborative project with CNRS-AMU, Marseille. Your Job: • Analysis of massively parallel spike and LFP data from the Vision for Action project (visuo-motor integration across areas) • Adaptation of machine learning algorithms and/or further development of methods for the analysis of these neuronal multichannel data • Development of methods for quantifying the relationship between spike patterns and LFP/ECoG data • Optimization and adaptation of neuronal network model in NEST for the comparison of experimental and simulated activity data • Implementation of data analysis tools in Python and integration of the codes into the shared analysis library Elephant • Contribution to the data analysis pipeline, under FAIR principles • Cooperation with experimental collaboration partners • Integration of the work into Ebrains / HBP • Presentation of project results at international conferences • Scientific and administrative project coordination • Support in the supervision of PhD students Your Profile: • MSc in physics, data science, mathematics or computer science; PhD in computational neuroscience, applied mathematics, physics or related • Detailed knowledge of neuroscientific concepts, especially in neuronal dynamics, statistical analysis of neuronal activity and dynamics • Expertise in network modelling at different spatial levels • Experience with large-scale data-science activities and scientific programming, especially Python • Fluent in spoken and written English • Experience in scientific publishing in a related field • Proactive working style, confident appearance and communication skills, high social competence and ability to work cooperatively in a team The position is initially for a fixed term of 2 Jahre years. Salary and benefits in conformity with the provisions of the Collective Agreement for the Civil Service (TVöD, salary grade 13-14). FZ Jülich promotes equal opportunities and diversity in its employment relations. We also welcome applications from disabled persons. Please apply via our recruitment system<https://www.fz-juelich.de/portal/EN/Careers/application/_node.html?cms_hre_…> under reference number: 2021-323. Martina Reske -- Dr. Martina Reske Scientific Coordinator Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6) Theoretical Neuroscience Jülich Research Centre and JARA Jülich, Germany Work +49.2461.611916 Work Cell +49.151.26156918 Fax +49.2461.619460 www.csn.fz-juelich.de<http://www.csn.fz-juelich.de> ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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The Gatsby Unit PhD programme
by Maneesh Sahani 02 Nov '21

02 Nov '21

Applications for the 2022 PhD Programme in theoretical neuroscience or machine learning at the Gatsby Computational Neuroscience Unit are currently open. Please send us your application by 14th November 2021 following the link on this page: https://www.ucl.ac.uk/gatsby/study-and-work/phd-programme/applications The Gatsby Unit is a research centre at University College London focusing on theoretical and computational neuroscience, computational statistics, machine learning and artificial intelligence; threads that are drawn together by our focus on the mathematical foundations of adaptive intelligent behaviour. We specialize in research and research training: We have a PhD programme and postdoctoral trainees, but no undergraduate or MSc programme. Our PhD programme provides a rigorous preparation for a research career. Although each student eventually chooses an advisor, the programme is highly interactive, and the Unit functions more like a single large research group loosely divided into subgroups than a regular university department. Many leading researchers in both machine learning and neuroscience have studied or worked here. Overall, about 2/3 of our alumni are in academia, many of them faculty members at the world's leading universities; others have founded companies (including DeepMind); and almost all are active in research. You can see our network of alumni here: https://www.ucl.ac.uk/gatsby/greater-gatsby Full funding is available to all students, regardless of nationality. Gatsby PhD studentships cover the cost of tuition at the appropriate rate, and include a tax-free stipend, presently £24,000 per annum. The Unit also welcomes applications from students with pre-secured funding or who are currently soliciting other scholarship/studentships. Further information on the programme can be found here: https://www.ucl.ac.uk/gatsby/study-and-work/phd-programme We particularly encourage applications from underrepresented groups. Questions should be directed to admissions(a)gatsby.ucl.ac.uk

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Master in Brain and Cognition, Barcelona
by Ruben Moreno Bote 02 Nov '21

02 Nov '21

Enrollment period for the Master in Brain and Cognition open! For Comp Neuro lovers! https:/https://www.upf.edu/web/mbc Why our master? (1) Barcelona, (2) Albert Compte + Gustavo Deco + Alex Hyafil + Klaus Wimmer + myself (Ruben Moreno-Bote) + top others, (3) hands-on teaching, with a 2-trimesters long project, (4) 2 new courses: "The Neural Basis of Memory" by Compte, and "The Brain in Action, Decision Making" by myself (5) highly international students (80% from abroad) + in English + highly competitive (accept only 25, typically from a list of 120 candidates) -- Rubén Moreno Bote Serra Húnter & ICREA Academia Professor Center for Brain and Cognition & Dept. of Information and Communications Technologies University Pompeu Fabra Campus Ciutadella c\ Ramon Trias Fargas, 25-27 Mercè Rodoreda building, 3th floor (room 24.331) 08005 Barcelona, Spain https://www.upf.edu/web/tcn https://twitter.com/morenobote <https://twitter.com/morenobote?lang=ca>

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job: machine learning research scientist at NIMH
by Francisco Pereira 02 Nov '21

02 Nov '21

## HIRING: machine learning research scientist The Machine Learning Team at the National Institute of Mental Health (NIMH) in Bethesda, MD, has an open position for a machine learning research scientist. The NIMH is the leading federal agency for research on mental disorders and neuroscience, and part of the National Institutes of Health (NIH). ## About the NIMH Machine Learning Team Our mission is to help NIMH scientists use machine learning methods to address research problems in clinical and cognitive psychology and neuroscience. These range from identifying biomarkers for aiding diagnoses to creating and testing models of mental processes in healthy subjects. Our overarching goal is to use machine learning to improve every aspect of the scientific effort, from helping discover or develop theories to generating actionable results. We work with many different data types, including very large brain imaging datasets from various imaging modalities, behavioral data, and picture and text corpora. We have excellent computational resources, both of our own (tens of high-end GPUs for deep learning, several large servers) and shared within the NIH (a cluster with hundreds of thousands of CPUs, and hundreds of GPUs). As a machine learning research group, we develop new methods and publish in the main machine learning conferences (e.g. NeurIPS and ICLR), as well as in psychology and neuroscience journals. Many of our problems require devising research approaches that combine imaging and non-imaging data, and leveraging structured knowledge resources (databases, scientific literature, etc) to generate explanations and hypotheses. You can find more about our work and recent publications at https://cmn.nimh.nih.gov/mlt ## About the position We are seeking candidates who are capable of combining machine learning, statistical, and domain-specific computational tools to solve practical data analysis challenges (e.g. designing experiments, generating and testing statistical hypotheses, training and interpreting predictive models, and developing novel models and methods). Additionally, candidates should be capable of visualizing and communicating findings to a broad scientific audience, as well as explaining the details of relevant methods to researchers in a variety of domains. Desirable experience that is not required, but will be considered very favorably: - deep learning - reinforcement learning - Bayesian statistical modelling - other types of modelling of human/animal learning and decision-making - neuroimaging data processing/ analysis (any MRI modality, MEG, or EEG) - other types of neural data (e.g. neural recording, calcium imaging) in the context of substantial research projects, ideally having led to submitted or published articles. Finally, you should have demonstrable experience programming in languages currently used in data-intensive, scientific computing, such as Python, MATLAB or R. Experience with handling large datasets in high performance computing settings is also very valuable. Although this position requires a Ph.D. in a STEM discipline, we will consider applicants from a variety of backgrounds, as their research experience is the most important factor. Backgrounds of team members include computer science, statistics, mathematics, and biomedical engineering. This is an ideal position for someone who wants to establish a research career in method development and applications driven by scientific and clinical needs. Given our access to a variety of collaborators and large or unique datasets, there is ample opportunity to match research interests with novel research problems. We also maintain collaborations outside of the NIH, driven by our own research interests or community impact. If you would like to be considered for this position, please send francisco.pereira(a)nih.gov a CV, with your email serving as cover letter. We especially encourage applications from members of underrepresented groups in the machine learning research community. If you already have a research statement, please feel free to send that as well. There is no need for reference letters at this stage. Other inquiries are also welcome. Thank you for your attention and interest!

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postdoc position on brain-machine interfaces at NYU & UO
by Luca Mazzucato 01 Nov '21

01 Nov '21

The Kiani (NYU) and Mazzucato (UO) labs are looking for a computational neuroscience postdoc to lead a project on closed-loop behavioral control in primates using a brain-machine interface. The candidate will develop a new theoretical framework to infer causal interactions from sparse sampling of large neural ensembles and deploy this method in the prefrontal cortex of behaving primates. The goal of this project is to innovate a technique for targeted manipulation of the prefrontal circuit and alter the monkey’s cognitive behavior in real-time, a key step toward advanced brain-machine interfaces and novel enhancing or therapeutic interventions in the human brain. The theory position will be co-supervised between the labs of Roozbeh Kiani at New York University and Luca Mazzucato at University of Oregon. The postdoc will be part of the vibrant neuroscience and machine learning communities at the Center for Neural Sciences (NYU) and at the Institute of Neuroscience (UO). The postdoc will be a member of the NSF-funded Accelnet consortium "International network for brain-inspired computation," fostering international collaborations between a large network of computational neuroscience/AI labs. Successful candidates will have a PhD in Physics, Neuroscience, Mathematics, Computer Science, Engineering, Statistics or a related discipline and, preferably, working experience in theoretical neuroscience and deep learning. Our labs strongly advocate for inclusivity in science and we encourage applications from underrepresented groups. For informal inquiries about the position please contact Roozbeh Kiani or Luca Mazzucato. Application reviews will start on Nov. 15 and will continue until the position is filled. Roozbeh Kiani & Luca Mazzucato

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