28 postdoctoral-fluid-dynamics PhD positions at Technical University of Denmark in Denmark
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of the highest quality within building design and processes, building construction and safety, building energy and services, solid mechanics, fluid mechanics, materials technology, manufacturing engineering
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design and processes, building construction and safety, building energy and installation, solid mechanics, fluid mechanics, materials technology, manufacturing engineering, engineering design and thermal
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, building energy and services, solid mechanics, fluid mechanics, materials technology, manufacturing engineering, engineering design and thermal energy systems. Technology for people DTU develops technology
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characterization aspect of the project, i.e. investigation of dynamics during catalyst activation and reaction by in-situ transmission electron microscopy. VISION is pioneering technology for visualizing catalytic
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, building energy and installation, solid mechanics, fluid mechanics, materials technology, manufacturing engineering, engineering design and thermal energy systems. Technology for people DTU develops
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for the PhD education . Assessment The assessment of the applicants will be conducted by Professor Tomislav Dragicevic and Postdoctoral Researchers Pere Izquierdo and Miguel Lopez. We offer DTU is a leading
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renowned research group for Gut, Microbes, and Health at the National Food institute, Technical University of Denmark (DTU). We offer a dynamic and sociable research environment with exiting challenges and
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projects partners. This requires good communications skills but also allows you to co-operate with leading European research institutes. Responsibilities and qualifications You will be part of a dynamic work
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI