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an established code (for example VASP, Quantum ESPRESSO). ● Research experience performing molecular dynamics simulations. ● Experience training, fine-tuning, or validating machine-learning interatomic
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performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the economic
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to simulations on quantum simulators and computers, and applications of machine learning, including neural quantum states. Candidates will benefit from state of the art computing resources at the Argonne
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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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interdisciplinary research hub dedicated to the study of how machine learning and AI algorithms are transforming societies. LCAL pursues one of the most urgent questions for contemporary society: how do we wish
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study