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frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well-documented code. Knowledge
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management. Background in computational modeling and machine learning. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background
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., MRI, EEG, and health records). Background in computational modeling and machine learning. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre
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Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning frameworks. Proficiency in
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of machine learning and multivariate statistical techniques for interpreting large-scale hydrodynamic and climatological datasets. Strong programming skills (e.g., Python, R, MATLAB, Fortran) and familiarity
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(AI) and its application across nursing and healthcare environments. The ideal candidate will possess cross-disciplinary fluency in AI technologies—including machine learning, data science, big data
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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position at the Associate/Full Professor level in Applied Mathematics, as part of a machine-learning focused cluster hire. The appointment will begin on August 16, 2025. Detailed Position Information