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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning