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) and Technical University of Denmark (DTU) aims at understanding and designing noble metal clusters in the non-scalable regime to enable exceptional catalytic performance for e.g. methane and ammonia
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Applications are invited for a 3-year PhD fellowship within the general field of coastal engineering at the Technical University of Denmark (DTU), Department of Civil and Mechanical Engineering
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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, or biophysics. Experience with experimental organic chemistry, NMR, kinetic modelling and/or cheminformatics are advantages. The candidate must be able to work independently, but also participate in
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functional priors from billions of years of evolution; how to compress measurements with controlled mixtures of molecules; and how to align models of laboratory experiments with observational human biology
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence
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advancing your scientific career at a leading research institution. You will join two research groups at the DTU National Food Institute, Technical University of Denmark: “Food Production Engineering” and