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

Master in Brain and Cognition, Barcelona

by Ruben Moreno Bote

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>

Nov. 2, 2021

job: machine learning research scientist at NIMH

by Francisco Pereira

## 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!

Nov. 2, 2021

postdoc position on brain-machine interfaces at NYU & UO

by Luca Mazzucato

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

Nov. 1, 2021