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the Department of Chemistry to develop innovative strategies for generating Machine Learning Interatomic Potentials (MLIPs) that accurately capture the dynamic nature of metal-ligand interactions. These models
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anomaly detection using advanced and optimized methods. • Literature review (image processing, deep learning, vision-language models, diffusion models, etc.). • Generative AI for creating reliable models
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multiscale modeling via machine learning force fields. This research will focus on applying a range of computational tools to realistic material systems, including interfaces and defects. This research will
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MED-ZSFG-CARD Full Time 87814BR Job Summary Zuckerberg San Francisco General Hospital and the Division of Cardiology is at the forefront of applying artificial intelligence and machine learning (AI
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, behavioral economics, and machine learning, to help policymakers identify and generate evidence on innovative approaches and policy solutions to their most pressing environmental and energy challenges. Job
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cloud computing platform for Machine Learning model development, and clinical researchers from NHS Greater Glasgow and Clyde. The successful candidate will also be expected to contribute
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background in physics or mathematics or interdisciplinary biophysics, and experience in machine learning is preferred. 3. Have published high-level research papers in related professional fields. 4. Have a
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 25 days ago
or related field # Numerical solution of partial differential equations # Scientific computing / high-performance computing # Algorithmic modeling and simulation # Fundamentals of machine learning or quantum
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with modelling and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. The division is found within the Department of Space, Earth and Environment
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of Molecular Biology (LMB), within a programme aimed at studying the neural circuit basis of behaviour. Specifically, to map the synaptic wiring diagram, or connectome, of whole brains, using machine learning