60 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" positions at NTNU Norwegian University of Science and Technology in Norway
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representations of time‑dependent data through sequences of iterated integrals and have recently gained significant attention in machine learning and data science. The project will investigate how
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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 Distributed machine learning takes advantage of communication and
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to work on cutting-edge research at the intersection of deep learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute
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selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Personal
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and research, in and outside academia. The position will specifically focus on Reinforcement Learning for Resource-Constrained Project Scheduling Problem (RCPSP). The Ph.D. candidate will be a member of
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to research, development and demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product
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universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Technology » Telecommunications technology Researcher Profile First Stage Researcher (R1) Positions PhD
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography. By linking defect