57 machine-learning "https:" "https:" "https:" "https:" "The Open University" positions at University of Copenhagen in Denmark
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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, grant applications, and research management such as supervision and training of research fellows and other staff. The successful applicant must also teach, supervise, prepare and participate in
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supervision and training of research fellows and other staff. The successful applicant must also teach, supervise, prepare and participate in examinations, and fulfill other tasks requested by the Department
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project, should contact Principal Investigator prof. Mikkel Bille, mbille@hum.ku.dk , phone +45 35329480 Link to the department’s website: https://saxoinstitute.ku.dk Introduction PhD studies consist
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of Health & Medical Sciences at the University of Copenhagen, commencing as soon as possible. Information on the department can be found at: https://bmi.ku.dk/english/about_department/ Information
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budget of DKK 3 billion. Learn more about The Faculty of Science at www.science.ku.dk You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ .
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working with large collaborative code projects Proficiency in applying AI-driven algorithms (e.g., neural networks, reinforcement learning) for the creation of surrogate models and the autonomous
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, please contact the principal supervisor Anne Sofie Hammer. General information about PhD study at the Faculty of Health and Medical Sciences is available at the Graduate School’s website: https