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, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences in electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials
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develops predictive, multi-scale computational frameworks to guide sustainable microbial food production. By coupling data science with mechanistic models, this collaboration between universities, research
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bioreactor performance. Developing and applying modeling tools to evaluate enzyme activity, and process performance across scales. Advance bioprocess optimization of complex microbial systems, including multi
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multi-carrier and cross-border energy grids, and efficiently activate flexibility services. These data-driven advances can deliver system-wide benefits, including lower operation and reserve-procurement
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safer, more reliable, and more sustainable renewable energy systems. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods
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, predictive modelling, and autonomous maintenance solutions. You thrive on scientific discovery, enjoy tackling complex multi-physics problems, and have the drive to explore innovative concepts
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. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods, machine learning tools, and simulation techniques. If you thrive
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, enjoy tackling complex multi-physics problems, and have the drive to explore innovative concepts at the intersection of engineering, data, and emerging technologies. DTU Wind and Energy Systems, in
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of greenhouse gases including CO2 and CH4. The PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning for inteGrated multi-parAmetric
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differentiation of human pluripotent stem cells into neurons, astrocytes and microglia Assessment of neuronal subtypes in the dish through qRT-PCR and immunocytochemistry Scaling up of stem cell cultures to high