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. Developing a computer model (“digital twin”) that link physical experiments with advanced control and operation strategies. The model will be utilized for techno-economic investigations regarding flexible
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merit and even better is knowledge of adaptive control, machine learning and AI. But the most important qualification is an eagerness to learn the mysteries of fuel-combustion-engine interaction. You must
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(EoS), or machine learning approaches. Hands-on experience in extracting bioactive compounds from biomass. Strong collaboration skills and the ability to work effectively in interdisciplinary teams. A
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knowledge of adaptive control, machine learning and AI. But the most important qualification is an eagerness to learn the mysteries of fuel-combustion-engine interaction. You must have a two-year master's
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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activities, i.e., teaching and supervision of BSc and MSc student projects at DTU. We are looking for candidates with Strong skills in AI, Machine Learning, and/or Data Science, preferably with experience in
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optimization frameworks that adopt an interdisciplinary approach, integrating concepts from operations research, transport modeling, welfare economics, transport justice and machine learning. You will be based
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM components are in use
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an extrusion machine that produces large-scale earth blocks Building a 3D printer that utilizes earth materials for construction purposes Developing numerical process models that simulate 3D earth printing