1,718 algorithm-development-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" PhD positions
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Description A PhD position starting November 1, 2025 (with some flexibility in both directions) is available at the University of Southern Denmark (SDU) for research in an exciting project in algorithmic
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A PhD position starting November 1, 2025 (with some flexibility in both directions) is available at the University of Southern Denmark (SDU) for research in an exciting project in algorithmic
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the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
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Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Previous relevant research experience Publications Programming skills (R or Python
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for recruitment positions and Regulations for the degrees philosophiae doctor (ph.d.) and philosophiae doctor (ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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candidates will undertake a minimum of 60 FTE business days of research and development with the organisation. The AGRS will support eligible students with a research internship scholarship (for 60 business
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“Research for a life without cancer" is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms