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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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. An interdisciplinary interdisciplinary environment, in which the candidate will be able to exchange across research fields (machine learning, bioinformatics, biology, and quantitative genetics) and applications (plant
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for quantifying methane emissions. A special focus will be on exploring how our local drone data can be combined with satellite observations, using machine learning and other modelling approaches, to improve
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied