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, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for
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, or a related field. Have documented experience in some of the following: Computational materials modelling or quantum mechanical simulations (e.g. DFT, MD). Machine learning / deep learning (preferably
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, 3, 12, 13): DC2: Infection biomarker discovery in chronic wound models DC3: Infection biomarker monitoring in environmental samples DC12: Optimizing bioreceptor function in interaction with graphene
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using proteins. Testing protein for immobilization. Testing of reactors for operation with real solutions. Modelling suitable reactors and evaluating options for scale-up. Responsibilities: Combine
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Job Description The Quantum and Nanophotonics section at DTU Electro is seeking an excellent and highly motivated PhD student to be a part of a program on ‘Symmetry-guided discovery of topological
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. Collecting and analyzing data from a full-scale (150 kWh capacity) pilot installation at DTU. Developing a computer model (“digital twin”) that link physical experiments with advanced control and operation
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and analyze a novel control approach for utility-scale multi-vendor HPPs, to tackle future power grids, characterized by increased complexity and multiple uncertainties. You are expected to model
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offered within the Marie Skłodowska-Curie QuNEST – Quantum Enhanced Optical Communication Network Security doctoral training program (https://qunest.eu ) and the Villum Investigator Program: Power-Efficient
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to the cyclotron motion of electrons near the plasma center. However, recent models predict that as these waves traverse the plasma edge, they may be influenced by non-linear plasma–wave interactions. Some studies