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of machine learning models, and exploratory data analysis. The Staller lab resides in the division of Genetics, Genomics, Evolution, and Development (GGED) in the Department of Molecular and Cell Biology. Max
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of state voting legislation for the Voting Laws Roundup. This work includes developing computational tools (e.g., using large language models, machine learning for text analysis and classification, etc
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engineering to lead multiple courses each year. Disciplines where we are seeking instructors include: Computational Chemistry Computational Quantum Chemistry Scientific Computing Machine Learning/Deep Learning
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contaminants using advanced mixtures and machine learning techniques; applying targeted and innovative non-targeted analysis methods to detect and characterize the presence of novel hazardous substances in
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epitaxy (MBE) growth of topological materials and machine learning. The successful candidate will play a leading role in advancing the frontiers of ARPES by integrating machine learning and data-driven
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Bioinformatics Machine Learning/Deep Learning High Performance Computing Complex mathematical modeling and simulations Computational Quantum Chemistry Leadership, management, and entrepreneurship in a
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modeling, and leverage machine learning techniques to support research objectives. Key Responsibilities: Conduct numerical simulations of giant planet atmospheric dynamics. Apply machine learning techniques
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. Use of radiation transport codes, especially MCNP, Serpent, OpenMC, or an equivalent code. Experience with uncertainty quantification methods. Experience with computer programming (Python, C
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, dynamic programming) and methods of causal inference. (20% time) Write computer code to maintain data and implement analyses; the applicant will have had substantial experience in manipulating large