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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization
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, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine learning) or a directly related field at the time of appointment is required. The successful applicant
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analysis, and/or machine learning (e.g., Python, Scikit-learn, PyTorch, etc.) is also desired. A track record of publishing in peer-reviewed journals on related topics is also strongly preferred. Application
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, Astronomy, or a closely related field is required. Experience with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must be submitted
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students, and may also teach one course per year for the Department of Statistics. A Ph.D. in Statistics, Biostatistics, Machine Learning, or a directly related field at the time of appointment is required
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for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine
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/machine learning and biomarker detection is a plus. The successful candidate will perform experiments and data analysis, assist with regulatory compliance, write follow-up grants, and disseminate findings
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calculations Materials modeling/electronic structure calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed
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, computer science, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep learning