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Field
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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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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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-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad expertise in AI and
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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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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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, finance or computing science/natural language processing/machine learning. Preference will be given to those who are proficient in Python, adept at processing large-scale data and have worked with large
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related field and have previous academic experience in machine learning. The candidate should have a strong background in metrology and medical image processing. Active participation and collaboration
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the integration of modern machine learning tools into computational catalysis workflows. Write research proposals for funding and manuscripts for publication. Assist the PI with the supervision of students and
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of scientific theory and methods Effective oral and written communication skills General computer skills and ability to quickly learn and master computer programs Excellent organizational skills and demonstrated