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chemicals. You will join a highly motivated and ambitious team with a great passion for bringing science, deep tech, and innovation into real-life solutions. Reformable expects to spin out from DTU at the end
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forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods
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. This may involve techniques such as spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint
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Steven Ludeke to discuss their expected degree timeline. Highly proficient in at least one statistical programming language (e.g., R, Stata, SAS, Python). Candidates that can show an aptitude for learning
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, climate solutions and energy efficiency. Learn more about our institute at https://www.sdu.dk/en/mmmi and the Centre at https://www.sdu.dk/cis . The Centre is autonomous but will collaborate closely with
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feed processing. For non-Scandinavian candidates an effort to learn to read, write, and speak Danish is a requirement. Contact Further information on the position may be obtained from Professor Jan Værum
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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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of machine learning, remote sensing and hydrology to evaluate and validate nature-based solutions that enhance local recharge and support the replenishment of shallow groundwater systems in dryland
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for time-resolved nano CT of sustainable cement”) Research and teaching efforts at a section and departmental level as appropriate and relevant (e.g., teach and co-supervise PhD and MSc student projects
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spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint for the construction industry and enable