October 2022 - Comp-neuro - lists.cnsorg.org

Fully funded Phd position in the intersection of neuroscience and AI

by Guozhang Chen

We (the group of Prof. Wolfgang Maass) accept applications for a fully funded Phd position for research in the intersection of neuroscience and AI. This Phd student will work with our team on reverse engineering data-based models for cortical microcircuits and their learning capabilities, as for example in --Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1 (G. Chen, F. Scherr, W. Maass).Science Advances (Nov. 2, 2022), https://www.biorxiv.org/content/10.1101/2021.12.07.471653v3?versioned=t <https://www.biorxiv.org/content/10.1101/2021.12.07.471653v3?versioned=t> --A solution to the learning dilemma for recurrent networks of spiking neurons (Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R., & Maass, W.) Nature Communications 2020, 11(1), 1-15. In addition, the Phd student will work on the development of brain-inspired architectures and algorithms for AI, like in --Local prediction-learning enables neural networks to plan (C. Stoeckl, W.Maass) https://www.biorxiv.org/content/10.1101/2022.10.17.512572v1 <https://www.biorxiv.org/content/10.1101/2022.10.17.512572v1>, and for highly energy-efficient neuromorphic hardware, as in --A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware (A Rao, P Plank, A Wild, W Maass), Nature Machine Intelligence 4 (5), 467-479. https://igi-web.tugraz.at/PDF/261.pdf <https://igi-web.tugraz.at/PDF/261.pdf> In order to qualify for this Phd-position you need to have a master degree in computer science or related areas, very good skills in mathematics/theory, and programming experience for GPUs. You should also have an excellent record,demonstrations of your research potential, and be strongly motivated to collaborate with us on understanding “how the brain works”. If you qualify, please send me your CV, transcripts, names of referees, as well as links to your master thesis, papers, and other pertinent documents to email:<maass(a)igi.tugraz.at> Guozhang Chen on behalf of Prof. Dr. Wolfgang Maass Institut fuer Grundlagen der Informationsverarbeitung Technische Universitaet Graz Inffeldgasse 16b , A-8010 Graz, Austria http://www.igi.tugraz.at/maass/Welcome.html <http://www.igi.tugraz.at/maass/Welcome.html>

Oct. 30, 2022

Faculty position in computational neuroscience/machine learning at Stony Brook University

by Giancarlo La Camera

Stony Brook University is currently running a faculty search for a computational neuroscientist. The position will be in the Department of Neurobiology and Behavior and will be filled at the Assistant or Associate Professor level. A competitive recruitment package will include state-funded salary and benefits, newly renovated lab space and competitive start-up funding. The ideal candidate is an outstanding scientist in theoretical neuroscience, neuro-AI or in statistics/machine learning approaches to investigate topical questions in neuroscience. Candidates from diverse and underrepresented backgrounds are especially encouraged to apply. The successful candidate will join a vibrant, collaborative and diverse group of neuroscientists at Stony Brook University and its affiliated institutions, and will actively contribute to the Department’s mission in research as well as undergraduate, graduate and medical student education. Applications should be submitted at the following URL: https://apply.interfolio.com/111422. Review of applications will begin by Nov 15th, 2022 and continue until the position is filled. -- Giancarlo La Camera <https://www.stonybrook.edu/commcms/neurobiology/people/faculty_Giancarlo_La…>, PhD Associate Professor of Neurobiology and Behavior Stony Brook University

Oct. 29, 2022

PhD program in Systems/Computational/Cognitive neuroscience at U of Oregon

by Luca Mazzucato

We are accepting applications to the Interdepartmental Neuroscience Graduate Program (INGP) <https://ion.uoregon.edu/training-programs/graduate-fellows> at the Institute of Neuroscience, a robust graduate training in Neuroscience across departments at the University of Oregon. The goal of INGP is to train students to think independently, creatively, and critically about problems in neuroscience. We aim to train students in a variety of skills that will prepare students for successful research, teaching, policy, or industry career. Students can enter INGP through the departments of biology, human physiology, psychology, mathematics, physics, computer and information science, and the Knight Campus, depending on their interests. The majority of our students enter through the department of biology, which allows students to complete three rotations during their first year, in order to help students identify a laboratory in which to do their dissertation research. Students who enter through psychology, physiology, and mathematics begin in their dissertation labs immediately. Regardless of how students enter INGP, they are provided with a set of mentors at the peer and faculty levels. All graduate students are required to teach for at least one academic year during their graduate career; at least a portion of this teaching takes place the first year. *Training Program* Students typically fall into a defined training program, however, training can be tailored to fit a student’s need and research interest. We offer two training tracks: Systems, Cognitive and Theoretical - Courses: cellular neuroscience, systems neuroscience, cognitive neuroscience, computational neuroscience, combinatorics, stochastic processes, neural networks. - Journal clubs: systems neuroscience, theoretical neuroscience. - Meetings: neural circuits and behavior, zebrafish groupie, joint theory lab meetings.

Oct. 28, 2022

Doctoral studies in Computational/Theoretical Neuroscience at NYU

by Eero Simoncelli

New York University is home to a thriving interdisciplinary community of researchers using computational and theoretical approaches in neuroscience. We are interested in exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain. A listing of faculty, sorted by their primary departmental affiliation, is given below. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Nevertheless, admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests. Center for Neural Science (CNS), Graduate School of Arts & Sciences (deadline: 1 December) [https://neuroscience.nyu.edu/program.html, and https://as.nyu.edu/cns/DoctoralProgram.html] * SueYeon Chung - NeuroAI and geometry. * Andre A. Fenton - Molecular, neural, behavioral, and computational aspects of memory. * Paul W. Glimcher - Decision-making in humans and animals. Neuroeconomics. * David Heeger (also in Psychology) - Computational neuroscience, vision, attention. * Roozbeh Kiani - Vision and decision-making. * Wei Ji Ma (also in Psychology) - Perception, working memory, and decision-making. * Tony Movshon - Vision and visual development. * Bijan Pesaran - Neuronal dynamics and decision-making. * Alex Reyes - Functional interactions of neurons in a network. * John Rinzel (also in Mathematics) - Biophysical mechanisms and theory of neural computation. * Cristina Savin (also in the Center for Data Science) - Computational models of learning and memory, machine learning. * Robert Shapley - Visual physiology and perception. * Eero Simoncelli - Computational vision and audition. * Xiao-Jing Wang - Computational neuroscience, decision-making and working memory, neural circuits. * Alex Williams - Statistical analysis of neural data. Neuroscience Institute, School of Medicine (deadline: 1 December) [https://neuroscience.nyu.edu/program.html, and https://med.nyu.edu/departments-institutes/neuroscience/] * Gyorgy Buzsaki - Rhythms in neural networks. * Dmitri Chklovskii (also in the Simons Foundation) - Neural computation and connectomics. * Biyu He - Large-scale brain dynamics underlying human cognition. * Dmitry Rinberg - Sensory information processing in the behaving animal. * Shy Shoham - Methods for controlling, imaging, and analyzing neural systems. * Mario Svirsky - Auditory neural prostheses; experimental/computational studies of speech production/perception. Psychology, Cognition & Perception program (deadline: 1 December) [http://as.nyu.edu/psychology/graduate/phd-cognition-perception.html] * Todd Gureckis - Memory, learning, and decision processes. * Brenden Lake (also in the Center for Data Science) - Computational modeling of cognition, deep learning. * Michael Landy - Computational approaches to vision. * Laurence Maloney - Mathematical approaches to psychology and neuroscience. * Denis Pelli - Visual object recognition. * Jonathan Winawer - Visual perception and memory. Mathematics (deadline: 18 December) [https://gsas.nyu.edu/admissions/arc/programs/mathematics.html] * David McLaughlin - Nonlinear wave equations, computational visual neuroscience. * Aaditya Rangan - Computational neurobiology, numerical analysis. * Charles Peskin - Mathematical biology. * Daniel Tranchina - Information processing in the retina. * Lai-Sang Young - Dynamical systems, statistical physics, computational modeling and theoretical neuroscience. Data Science (deadline: 12 December) [https://cds.nyu.edu/phd-program/] * Joan Bruna (also in Computer Science) - Machine learning, signal/image processing. * Kyungyun Cho (also in Computer Science) - Machine learning, natural language processing. * Carlos Fernandez-Granda (also in Mathematics) - Optimization methods for medical imaging, neuroscience, computer vision. * Grace Lindsay (also in Psychology) - Machine learning models of the brain/mind. * Mengye Ren (also in Computer Science) - Machine learning, computer vision, representation learning. Physics (deadline: 30 December) [https://as.nyu.edu/physics/programs/graduate.html] * Marc Gershow - Perception, decision-making, and learning in neural circuits. Computer Science (deadline: 12 December) [http://www.cs.nyu.edu/home/phd/] * Davi Geiger - Computational vision and learning. * Yann LeCun - Machine learning, computer vision, robotics, computational neuroscience. Economics (deadline: 18 December) [https://as.nyu.edu/econ/graduate/phd.html] * Andrew Caplin - Economic theory, neurobiology of decision. * Andrew Schotter - Experimental economics, game theory, neurobiology of decision.

Oct. 28, 2022

EPFL - Faculty position at the intersection of computational and life sciences

by Mathis Alexander

Dear colleagues, The Schools of Computer and Communication Sciences (IC) and Life Sciences (SV) at EPFL invite applications for a faculty position at the intersection of computational and life sciences. We seek candidates who use machine learning and/or other original computational approaches to solve challenges in life sciences. The appointment could be either at the assistant professor or tenured level. Candidates must have an outstanding academic record, a compelling vision, demonstrated impact, and a strong commitment to excellence in teaching and mentoring students. EPFL attracts top students from all over the world and offers competitive salaries, generous research funding and excellent research infrastructure. Switzerland has an exceptionally high human development index and consistently ranks highly in quality of life, economic competitiveness, and innovation. Applicants must submit a cover letter, a curriculum vitae including a publication list, brief statements of research and teaching interests, and contact information of at least 3 references (5 for tenured positions) who are ready to supply a letter upon request. To apply, go to: https://facultyrecruiting.epfl.ch/position/40599570 Screening will start on December 1, 2022, but applications submitted after this date will also be considered. Questions can be addressed to: Profs. George Candea and Matteo Dal Peraro Co-chairs of the Faculty Recruiting Committee E-mail: recruiting.icsv(a)epfl.ch<mailto:recruiting.icsv@epfl.ch> For more information on our schools, please visit https://sv.epfl.c<https://sv.epfl.ch/>h<https://sv.epfl.ch/> and https://ic.epfl.ch<https://ic.epfl.ch/> EPFL is an equal opportunity employer and family-friendly university. It is committed to increasing the diversity of its faculty. It strongly encourages women to apply.

Oct. 26, 2022

EPFL AI4Science Postdoctoral Fellowship: Deadline Oct 31st.

by Mathis Alexander

Dear colleagues, The EPFL AI4Science Fellowship gives outstanding and highly motivated young researchers the chance to collaborate with a broad range of EPFL faculty on accelerating the use and furthering the understanding of machine learning methods in the scientific discovery process. The Fellowship focuses on excellence and interdisciplinarity. Therefore fellows will work with several laboratories at EPFL and will be granted ample freedom to develop their own research agenda. We expect fellows to contribute to a vibrant and multidisciplinary community of researchers passionate about the transformation of science with new data-driven methods. Initial appointments will be made for one year, with the expectation of renewal for at least a second year, upon agreement of the AI4Science scientific committee. The salary follows the generous scale for EPFL post-doctoral researchers. Starting date is flexible. Application deadline: October 31st 2022 More details are available at https://www.epfl.ch/research/domains/cis/epfl-ai4science/call-for-fellows/ Best wishes, Alexander Mathis [cid:clip_image001.png] Alexander Mathis Tenure Track Assistant Professor people/alexander.mathis mathislab.org<http://mathislab.org>

Oct. 26, 2022

Neuroscience Gateway (NSG) Virtual workshop as a part of Society for Neuroscience Annual meeting, San Diego Nov 2022

by Majumdar, Amitava

Hello, We are happy to announce that the Neuroscience Gateway (NSG) will host a Virtual workshop as a part of the Society for Neuroscience Annual Meeting in San Diego, November 2022. Date and time of the virtual NSG workshop: November 12, 8:30 AM - 12:30 PM USA Pacific Time Zone. We have distinguished speakers who are early career researchers to senior researchers. They will present their work, many of which utilize NSG or are neuroscience software projects and are available on NSG for neuroscientists to use on high performance computing resources. Registration is free but required by November 9th. Please register if you plan to attend. Title of the virtual workshop: Neuroscience Gateway - Enabling Large Scale Modeling, Data Processing and Dissemination of Software Details about the list of speakers, abstract of talks and registration are available here: http://www.nsgportal.org/NSG_HPAC_SfN2022_Wkshop.web.htm Thank you. Regards. NSG Team

Oct. 26, 2022

Faculty positions at Washington University in St Louis

by Geoffrey Goodhill

Electrical & Systems Engineering: Tenure-Track Faculty Positions Washington University in St Louis https://apply.interfolio.com/114838 The Electrical and Systems Engineering department at Washington University in St. Louis invites applications for multiple tenure-track faculty positions with an effective start date on or after July 1, 2023. Candidates should have earned a Ph.D. or equivalent degree in electrical engineering, systems engineering, computer engineering or a closely related discipline. Washington University is a highly-selective national research university with a strong tradition of research excellence. The University’s strategic plan, released in 2022, seeks growth of top-tier research, scholarship, and creative practice with an emphasis on transdisciplinary and cross-school research. Included in this plan are foci related to applications in medicine, public health, infrastructure, and addressing pressing societal challenges. Accordingly, we seek both junior and senior applicants who will contribute to research efforts across ESE, including in several emphasis areas: information and decision systems, including autonomous systems and human-machine interaction network science, including social systems and large-scale data analysis imaging science, including signal processing and computational imaging engineering brain science, including theory, models and methods for brain-inspired computation and for measuring and manipulating neural circuits quantum sensing and imaging, including science and technology for detection and estimation beyond classical physics. Candidates interested in this area are encouraged to note University-wide growth in quantum technology at https://quantumleaps.wustl.edu/. Candidates may apply to the quantum cluster search in addition to the ESE search. Equity, diversity and inclusion are core values at Washington University, and candidates will need to demonstrate the ability to create inclusive classrooms and environments in which a diverse array of students can learn and thrive. Candidates who will contribute to the diversity of the department are strongly encouraged to apply. Successful applicants will have a primary appointment in the department of Electrical and Systems Engineering (ese.wustl.edu) with the possibility of joint appointments in other departments. The faculty member will be expected to teach undergraduate and graduate courses in electrical and systems engineering, participate in university service, and establish a thriving externally-funded research program. Faculty positions are open for all levels. Applications should include: (1) a cover letter that identifies the candidate’s three most significant publications and describes their interest in the position; (2) a curriculum vitae; (3) a research plan for the next five years that should not exceed three pages, and should highlight the problem(s) or set of questions to be investigated, the envisioned approach, a mentoring strategy, and the proposed funding sources; (4) a statement of teaching interests and philosophy; (5) a statement describing contributions to and future plans for enhancing equity, diversity and inclusion; and (6) a list of at least three references to (https://apply.interfolio.com/114838) Priority will be given to completed applications (including submitted reference letters) received before December 15, 2022. However, applications will be accepted at any time and will be considered until the positions are filled. Professor Geoffrey J Goodhill Departments of Neuroscience, Developmental Biology, Biomedical Engineering, Computer Science and Engineering, and Electrical and Systems Engineering Washington University in St Louis St. Louis, MO 63110 g.goodhill(a)wustl.edu https://neuroscience.wustl.edu/people/geoffrey-goodhill-phd

Oct. 26, 2022

Postdoc positions at Harvard

by Cengiz Pehlevan

Dear colleagues, I want to point to a few postdoctoral fellow positions at Harvard for candidates in computational/theoretical neuroscience: 1. The George F. Carrier Postdoctoral Fellowship: https://academicpositions.harvard.edu/postings/11642 <https://academicpositions.harvard.edu/postings/11642> 2. Postdoctoral Fellow in Theoretical Neuroscience and/or Theory of Neural Computation: https://academicpositions.harvard.edu/postings/11671 <https://academicpositions.harvard.edu/postings/11671> 3. The Harvard Data Science Postdoctoral Fellows: https://datascience.harvard.edu/data-science-postdoctoral-fellows <https://datascience.harvard.edu/data-science-postdoctoral-fellows> 4. Postdoctoral fellow in the Foundations of Machine Learning: https://academicpositions.harvard.edu/postings/11762 <https://academicpositions.harvard.edu/postings/11762> Please feel free to write to me with questions. Best wishes, Cengiz

Oct. 25, 2022

INCF/OCNS SoftwareWG Dev session: Denis Alevi: Brian2CUDA: November 3, 2022, 1600 UTC over Zoom

by Ankur Sinha

Dear all, Please join us for our next dev session on Brian2CUDA: https://ocns.github.io/SoftwareWG/2022/10/18/dev-session-denis-alevi-brian2… Denis Alevi will introduce the Brian2CUDA tool in this session, and discuss its development. We will also have a discussion on GPU based simulation in neuroscience after the presentation. - Date: Thursday, November 3, 2022, 1600 UTC (Click here to see your local time: https://www.timeanddate.com/worldclock/fixedtime.html?msg=Dev+session%3A+De…) - Location (Zoom, login required): https://ucl.zoom.us/j/95692778384?pwd=VldIQ3hPTU1zczNpYjQxSSt4Z25xdz09 The abstract for the talk is below: Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian’s CPU backend. Currently, Brian2CUDA is the only package that supports Brian’s full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists. References: - Publication: Brian2CUDA: flexible and efficient simulation of spiking neural network models on GPUs: https://www.frontiersin.org/articles/10.3389/fninf.2022.883700/abstract - Documentation: https://brian2cuda.readthedocs.io/en/latest/ - Source code: https://github.com/brian-team/brian2cuda We hope to see you there. On behalf of the INCF/OCNS Software working group, -- Thanks, Regards, Ankur Sinha (He / Him / His) | https://ankursinha.in Research Fellow at the Silver Lab, University College London | http://silverlab.org/ Free/Open source community volunteer at the NeuroFedora project | https://neuro.fedoraproject.org Time zone: Europe/London

Oct. 25, 2022