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, microbiology, infectious diseases, animal agriculture, or food safety. Experience in applied statistics, data science, machine learning, mathematical modeling, epidemiology, disease ecology, and PCR assay
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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
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related subject, or be close to completing their qualification. They should have experience in systems engineering, simulation, digital modelling, machine learning, artificial intelligence, or digital twin
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., machine learning, data science, computer science, biostatistics, epidemiology, or another quantitative field), and applied experience in machine learning, with extensive hands-on experience of model
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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close to completing their qualification. They should have experience in systems engineering, simulation, digital modelling, machine learning, artificial intelligence, or digital twin (model, shadow, twin