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Field
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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how workplace contexts shape experiences of impostorism and how these experiences affect career development. Grounded in Social Identity Theory, the project aims to transform the way researchers
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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PhD (within the last 0-5 years) in chemistry, physics, chemical engineering, or a closely related field A strong foundation in quantum mechanics and electronic structure theory Demonstrated expertise in
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generate the first global, theory-driven evidence base on participatory sustainability governance, and advance both social science methodology and governance theory. The project team comprises the Principal
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research reports and technical papers for publishing. 3. Investigates the feasibility of applying a wide variety of scientific principles and theories. 4. Maintains substantial knowledge of state-of-the-art
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how clinical blood tests are used. The chosen candidate will develop novel theory, large-scale data pipelines, and collaborate with clinicians to drive impact from these models, and use them to benefit
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written and oral form. Desired skills/knowledge: Experience with excited-state electronic-structure theory, nonadiabatic dynamics, surface hopping, path-integral methods. Experience with active learning
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 15 hours ago
their field of study Engage with new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community
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-performance computing, particularly in the theory of efficient string and data structures, sequence indexing, and large-scale data analysis. Experience with the Burrows–Wheeler transform, pattern matching, and