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Field
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Stanford Departments and Centers: Environmental Social Sciences Postdoc Appointment Term: 12 months renewable for an additional 2 years based on satisfactory performance and availability of funding
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leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation
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or physiology. Further, postdocs should have a deep interest in scientific collaboration between researchers using theoretical and empirical approaches. The five projects are: A. Sensing the breaking point
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infrastructure, mobility demand, and power grid operations. On top of this environment, a deep-learning-based learning will be developed to enable decentralized and coordinated decisions on EV user charging
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heterogeneity in cancer, inflammation, and tissue senescence. • Developing next-generation deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial
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, deep learning, fNIRS, eyetracking). The postdoc will primarily be mentored by Meg Cychosz (Linguistics), but will have ample opportunity and flexibility to establish collaborations with other groups and
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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subjects and research areas; Experience with one or more general purpose programming languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; An interest in
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
. Preferred Qualifications, Competencies, and Experience Programming skills (e.g., python, bash scripting, Fortran, C++ and CUDA), expertise in computational modeling (such as Deep Learning, Molecular Dynamics