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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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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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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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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
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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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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three