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or population genetics, or a related discipline. You should have relevant coding experience and a strong background in software development, algorithm design, and the production of high-quality scientific
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
related quantities obtained from non-local matrix elements, as well as on the development of algorithms and software for large-scale lattice QCD simulations on DOE leadership-class computing facilities
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Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote sensing and open-source GIS tools (e.g. Google
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-driven research group working at the intersection of computational genomics, clinical artificial intelligence, and imaging genetics. This position offers an exciting opportunity to develop novel algorithms
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. in genetics, cancer biology, computational biology, bioinformatics, computer science, systems biology, or a related field with relevant experience. Candidates from quantitative disciplines, including
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. The selected candidate will contribute to one or more research projects within the Systems Genetics group led by Professor Enrico Petretto (Principal Investigator). These project(s) will be conducted
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. The student will support research activities including implementing and testing algorithms, conducting experiments, reviewing technical literature, analyzing results, and assisting with research publications
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quantitative genetics and maize breeding. -Utilizes skills and knowledge in these and other areas to complete research projects leveraging new data extraction and analysis algorithms and connecting
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period of development known as adolescence. Furthermore, we will employ reinforcement learning models and machine learning algorithms to uncover how changes in neural activity drive changes in decision
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/equilibrium reconstruction from limited, noisy diagnostic measurements. Scenario Optimization: Develop plasma scenario optimization workflows leveraging nonlinear programming, genetic algorithms, and