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, or landscape modelling Further, we will prefer candidates with some of the following qualifications: Teaching and supervision experience at the BSc and MSc level Interest and preferably experience in developing
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engineering). The strategic focus of the department is to be a leading force within digital health and to be well-known for medical doctors and engineers collaboratively developing solutions together
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is established with the ambition to become one of the leading research centers developing the theory of heterogeneous catalysis and electro-catalysis. This includes development of a fundamental
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on the development of anti-infective drugs and vaccines using the pig as a model platform. Main tasks will include the design, coordination, and execution of pig experiments, and assessment of drug and vaccine
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(e.g., network calculus or similar timing-analysis methods) and/or dynamic reconfiguration (e.g., using Q-search, reinforcement learning, or metaheuristics). You may also contribute to developing
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immediate biomedical relevance Access state-of-the-art labs and infrastructure at DTU Health Tech Collaborate with an interdisciplinary team of chemists, engineers and biologists Develop your academic career
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studies with immediate biomedical relevance Access state-of-the-art labs and infrastructure at DTU Health Tech Collaborate with an interdisciplinary team of chemists, engineers and biologists Develop your
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design and FPGA programming. We are seeking a highly motivated and talented Postdoc to join our group and contribute to our efforts in developing integrated optical phased array (OPA) on a thin-film
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Two year postdoc position at Aarhus University for single molecule FRET based investigations of l...
concerns an integrative effort where several cryo-EM structures are used to develop donor and acceptor labelled proteins and complexes to follow their large scale rearrangements by single-molecule
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with