21 parallel-computing-numerical-methods positions at University of California in Postdoctoral
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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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Berkeley Lab’s (LBNL ) Biological Systems and Engineering (BSE ) Division has an opening for a Computational Postdoctoral Fellow to join the Quantitative Modeling Group led byHéctor García Martín
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Description Multiple postdoctoral fellowship opportunities are available with The Institute for Emerging CORE Methods in Data Science (EnCORE), a TRIPODS Phase II institute funded by the National
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, environmental engineering, or a related field. Experience in numerical hydrologic models and land surface models. Experience with Linux on cluster and/or high-performance computing environments. Experience
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge
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Luis W. Alvarez Postdoctoral Fellowship and Admiral Grace M. Hopper Postdoctoral Fellowship in Computing Sciences The Computing Sciences Area at Lawrence Berkeley National Laboratory is now
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methods for materials science applications. Strong organizational skills including the ability to prioritize work, meet deadlines, and contribute to the planning of a scientific research program. Excellent
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on their background and interests and the expertise of project principal investigators. These methods may include: ● benthic ecology surveys and sampling ● direct vessel-based observation of gray whale feeding ecology
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laboratories. What is Required: A recent Ph.D. (within the last 1-2 years) in Computer Science, Chemical Engineering, Systems Biology, Bioengineering, Computational Biology, or a related discipline. Strong
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. Department of Energy’s ESS-DIVE repository. The DOE Biological and Environmental Research (BER) program produces uniquely valuable datasets increasingly used in AI/ML, but many are not AI-ready due