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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
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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integrate machine learning algorithms and Earth System Models to emulate carbon processes in the ocean connected to the biological activities. You will be enrolled in DTU’s Section for Oceans and Arctic and
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contribute to the teaching activities of the section of Mechanical Engineering by teaching 1-2 courses per semester. You will focus on developing and extending in-house computational codes based on open-source