73 computational-biology-physics-training Postdoctoral research jobs at Technical University of Denmark in Denmark
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valuable biomolecules from side streams and reusing them in food applications. The successful applicant will be working within the Section of Synthetic Biology at the Department of Biotechnology and
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to relevant external sources. By enabling data interoperability across facilities and process units, this infrastructure will allow real-time coordination, intelligent scheduling, resource sharing, and improved
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: Review and synthesize epidemiological evidence to estimate health impacts of foods, considering both risks (chemical, microbiological, nutritional) and benefits (nutritional). Develop dietary exposure
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expertise in several of the following areas, and a strong motivation to develop further: Protein chemistry and structural biology Enzyme kinetics and mechanistic studies Advanced enzymology and assay
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: Background in Data Science, Computer Science or related fields; Working experience in implementing AI models (not just loading pre-trained model). PyTorch framework is preferred; Experience with APIs
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design and execute synthetic routes to oligosaccharides and glycoconjugates. You will join a research lab with a broad interest in chemical biology and a long-standing interest in plant cell wall glycans
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forefront of fiber and quantum photonics. At DTU Electro, you will take a central role in the experimental demonstration of third-order spontaneous parametric down-conversion (TOSPDC), a process capable of
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the same time develop your academic and personal skills. The Power and Energy Systems division (PES) is part of the renowned Department of Wind and Energy Systems, DTU. PES resides at two campuses (Risø and
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and bio-technological informatics, metagenomics, epidemiology, integrative systems biology and machine learning. The research at DTU Bioinformatics is focused on bioinformatics and computational
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, you will contribute to research-based teaching and the supervision of student projects. Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and