10 programming-"Multiple"-"U"-"Prof"-"O.P"-"UCL"-"U.S"-"Fraunhofer-Gesellschaft" PhD positions at Technical University of Denmark
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with project coordination and collaboration across multiple stakeholders You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year
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decision-support or safety-critical systems. Knowledge of Cyber-Physical Systems, Sensor Fusion, or Risk Management is an advantage. Excellent programming skills (Python, C++, or similar). Ability to work in
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carried out and data is generated? Then keep reading. The Natural Products Genome Mining Section at DTU Biosustain studies multiple aspects of Actinomycete bacteria, for example their genomics, molecular
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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explored using scientific machine leaning. Machine learning, programming experience and a curious mind-set You are fascinated by how computers can learn from data and you have a strong interest in the
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optimization. Experience with energy system modeling - ideally of large scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire
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both technical insight into data modeling and a solid understanding of how real-world engineering data is generated, structured, and used. We are seeking motivated candidates with strong programming
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part of the Perception and Cognition for Autonomous Systems Group (PCAS), and you will find yourself among multiple PhD students and senior researchers working on multiple aspects of autonomous robotics
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oxidation and disinfection processes. Work together with researchers at multiple departments at DTU and NTNU Research activities will mainly be carried out at DTU Sustain with research visits in Norway
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development and marine management. Your primary tasks will be to: Compile and harmonize data from multiple sources (e.g., EMODnet, Copernicus, fisheries surveys, citizen science). Engage with data managers and