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machine learning and quantum information theory. The position focuses on theory but can potentially collaborate with experiments closely. The detailed topics can be, but not limited to, quantum metrology
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the Research Ireland-funded project Inverse Problems for Sustainable Living in Ireland (IP-SUSTAIN). The project aims to advance both the mathematical theory and the computational methodologies of Inverse
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-scale optimization (inverse design). Prior experience with both computational photonics and principles of scattering theory and photonic crystals. Prior collaboration on photonic/semiconductor fabrication
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: exact diagonalization, Monte Carlo, and where appropriate, tensor network techniques. The postholder should also have a broad familiarity with the theory of strongly correlated systems and excellent oral
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 22 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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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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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 principles and theories
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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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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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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