77 density-functional-theory Postdoctoral positions at Technical University of Denmark
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our team at DTU Compute, offering a fully funded position within a dynamic and interdisciplinary research environment. The positions are part of the research project “AI-driven materials optimization
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qualification, you must hold a PhD degree (or equivalent). We offer The position offers a unique possibility of being part of an interdisciplinary research team working on building foundational knowledge linking
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collaboration with world class experts? You will play a key role in a collaboration between Professor Alex Tofgaard Nielsen at DTU Biosustain and the Microbial Expression Engineering Department at Novo Nordisk
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idea within the value chain of functional coatings from raw materials to end-product. Coating formulation and production principles Optimising the production line by i.e. turning towards in-line
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Job Description Are you driven by the potential of AI in enzyme engineering? Do you want to design novel enzymes from scratch using cutting-edge generative models and validate their function in
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Postdoc in development and testing of electrodes for liquid alkaline water electrolysis - DTU Energy
, this might be a position for you. If you moreover like to work with applied research in close contact with the power-to-X industry, you have come to the right place. Responsibilities and qualifications You
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. The funding body is the Novo Nordisk Foundation. In parallel with research, you have the opportunity to take part in innovation activities with Copenhagen Microsystems , and this could be up to 20% of your time
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of single indistinguishable photons for photonic quantum information technology. Single-photon sources play an important role in photonic quantum information applications, which generally require near-unity
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). We are looking for a candidate with a background in engineering. The successful candidate should possess: excellent interpersonal and collaboration skills, which are required to work with others, in a
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FGFR isoforms. Recent advances in AI-driven protein design offer new opportunities to revisit the long-standing challenge of selectively targeting FGFRs. The project will use state-of-the-art AI-based