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the world’s largest supercomputers (Polaris, Aurora) and some of the most advanced characterization tools in the world at Argonne and Sandia National Labs. Candidates with a background in deep learning
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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(or near completion) in computer science, machine learning, statistics/biostatistics, computational biology, data science, physics, or a related field. Experience with modern deep learningframeworks (e.g
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networks, financial networks, temporal and dynamic graphs, uncertain graphs, explainability and fairness of deep learning methods on networks. The aim will be publishing several research papers at top-tier
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into global environmental changes, and ecosystem sustainability Experience with deep learning, radiative transfer modeling Teaching and supervision experience Who we are At the Department of Agroecology, our
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responsibilities: 1) The postdoc will work with the PI to develop a project in one of the two research directions, to comprehensively read literature, to establish appropriate experimental systems, to learn
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Science or related field. Experience in one or more ML domains, such as deep learning, reinforcement learning, or human-centered ML. Proficiency in programming languages (e.g., Python) and ML frameworks (e.g
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) About the Project Deep learning models, and in particular large language models (LLMs), have demonstrated remarkable capabilities but remain limited by their heavy computational requirements, lack
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and develop skills in the growth of cultured tissue, an area of growing academic and industrial interest Opportunities to develop skills in biophysical models of tissues and deep learning techniques Key
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application of advanced deep learning models, with an emphasis on techniques such as knowledge distillation. The candidate will engage in research involving time-series analysis, including modeling, forecasting