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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | 11 days ago
health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental biology, chemistry, and medicine
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in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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integrated modeling and machine learning for energy and circular-economy applications. The Post Doctoral Associate will design and execute simulation and machine-learning studies, analyze results
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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among
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reactions. (2) Prof. Francesca Grisoni ( https://molecularmachinelearning.com/ ) leads the Molecular Machine Learning Group at the Technical University Eindhoven and will lead a project on designing
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Master Thesis - Decoding Occupancy: Classifying User Activity from Building Power Consumption with Machine Learning At the Institute of Climate and Energy Systems Engineering (ICE-1) we focus on the
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AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a specific focus on: Data-Centric AI: Including
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<span lang="en"> Helmholtz-Zentrum Hereon</span> | Geesthacht, Schleswig-Holstein | Germany | about 20 hours ago
Material and Process Design at the Helmholtz-Zentrum Hereon is offering a 4-year PhD position in the field of computational materials engineering, machine learning, and digital twins for advanced
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details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email [email protected] Applications will
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Title: Modelling dynamics and reactivity with Machine Learning Interatomic Potentials Supervisor: Professor Fernanda Duarte Start date: 1 st October 2027 Applications are invited for a fully funded