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PhD position: Develop Hybrid Machine Learning for Global Soil Mapping Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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PhD student in generative modeling for data-efficient machine learning Norrkoping Reference number LiU-2026-02831 We are now looking for a PhD student in machine learning with a focus on generative
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PhD student focusing on Machine Learning of Geometric Representations at ISY Linkoping Reference number LiU-2026-03853 We have the power of over 50,000 students and co-workers. Students who provide
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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | about 18 hours ago
Stakkevollan 9037 Tromsø Norway The address could not be found. About the positions Interested in pursuing a PhD in machine learning in an interdisciplinary and collaborative environment in Norway? Integreat
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Job Description Are you passionate about science and developing and applying machine learning (ML) methodology to study the immune system and associated autoimmune disease states and mechanisms
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fellowships in machine learning await. Collaborate, innovate, and thrive! PhD Fellowships in Knowledge-Driven Machine Learning in Norway (8 positions) Apply for this job See advertisement About the positions
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complex, uncertain environments. This PhD project focuses on developing novel algorithms and theoretical foundations for machine learning, with particular emphasis on reinforcement learning, bandit
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more robust computational descriptions of complex chemical reactions. The project combines ideas from quantum chemistry, machine learning, numerical optimization, and molecular modelling. The long-term
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The always-on, safety-critical nature of air traffic control raises rich and exciting challenges for machine learning and AI. The University of Exeter in partnership with NATS, the UK’s main air
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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