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of Civil and Mechanical Engineering, Thermal Energy Section. We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics
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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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, emmva@dtu.dk . You can read more about the Department of Applied Mathematics and Computer Science at www.compute.dtu.dk and about the Section for Cybersecurity Engineering at www.compute.dtu.dk/sections
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Species interact in complex biological networks e.g. foodwebs, forming the foundation of natural ecosystems. Yet, we lack the tools to predict how these networks change in time and space. This is
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on developing machine-learning-based or statistical emulators to approximate key outputs of complex Earth System Models, with the aim of enabling efficient uncertainty quantification, sensitivity analysis, and