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computing can unlock new optimization and control strategies in future power systems. Our projects are conducted in an international and multidisciplinary environment, bringing together expertise from across
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programme. Academic staff contribute to the teaching. English is the preferred language in the laboratory, at meetings and at seminars. The department employs approx. 500 people from all over the world, and
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our team at DTU Compute, offering a fully funded position within a dynamic and interdisciplinary research environment. The positions are part of the research project “AI-driven materials optimization
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large part of the medical degree programme. Academic staff contribute to the teaching. English is the preferred language in the laboratory, at meetings and at seminars. The department employs approx. 500
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
regions of the world, and at the same time impact the climate positively utilizing CO2 as raw material. To be launched into scaled solutions, the technology needs to be optimized through leveraging digital
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regions of the world, and at the same time impact the climate positively utilizing CO2 as raw material. To be launched into scaled solutions, the technology needs to be optimized through leveraging digital
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, or reinforcement learning Proficiency in Python and contemporary software ecosystem for optimization, dada management, and API development Experience with teaching and supervision Experience with IoT, edge computing
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be
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partners and optimize them for nonlinear quantum processes Collaborate with theory colleagues to refine fiber designs based on experimental feedback Disseminate results at international conferences and in