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Fermilab, Computational Science and Artificial Intelligence Directorate Position ID: FNAL-CSAID-RESEARCHASSISTANT [#30908, FermiEAC-RA] Position Title: Position Type: Postdoctoral Position Location
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emphasis on questions grounded in data that are generated by human activity, including computational social science (e.g., algorithmic accountability and the interplay of data science with policy, law, and
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membership to Academic Impressions, LinkedIn Learning, and UT Dallas Bright Leaders Program. Visit https://hr.utdallas.edu/employees/benefits/ for more information. If you are looking for a rewarding career
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: genetics, epigenetics, inflammation, metabolic pathology, autoinflammatory pathology, autoimmunity, arthritis, computational analysis, mathematical modeling, applied algorithms, machine learning in biology
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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Learning, Theoretical Computer Science (Discrete Mathematics, Algorithms, etc.). Experience with EdTech tools, such as Ed Discussion, Gradescope, GitHub Classroom, Canvas, etc. Ability to respond on short
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the results and improving the algorithms. It will also involve extensive literature reviews. Applicants must have an undergraduate degree in computer science, have significant experience with deep learning with
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algorithms for the next generation of particle physics experiments and also explores other ways AI can accelerate scientific discovery. The group collaborates closely with computer scientists, astrophysicists
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
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, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods, the candidate will be positioned at the forefront of genetic data science