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Field
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 1 month ago
Job description:Postdoctoral Researcher (f/m/d) in Machine Learning and Surrogate Modeling for Geochemical Systems With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500
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The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs), uncertainty quantification, and atomistic simulations within the FNR
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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Details Title Postdoctoral Fellow in Computer Science — From Theory to Practice: Reinforcement Learning for Large Scale Foundation Model Post‑Training School Harvard John A. Paulson School of
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About us: The applicant will join the Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will
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infertility, pregnancy, lactation and developmental programming, urogynecology, artificial intelligence and machine learning are particularly encouraged to apply. The Department of Obstetrics and Gynecology
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information, is multitasking. However, we are notoriously poor multitaskers. A recent framework proposes that this is due to the fact that tasks often share representations, which can be beneficial for learning