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. For example, in most studies in medicine, fatigue is measured by asking how fatigued people felt “in the last month”. To drive innovation in this research area, the main aim of this project is to work
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critical for optimizing performance and is an important ingredient in the design of high performance parts in aeronautics, energy, transport, and plenty of other domains. The theory behind the aforementioned
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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on the mathematical theory enabling these advances, via asymptotic analysis and nonparametric modeling. You will work in the Statistics section at the Delft Institute of Applied Mathematics (DIAM
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-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and machine learning approaches. These are similar to earlier work
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developing new explanation methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as
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students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position PhD project: Intra-body Semantic Communication
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of density functional theory (DFT) is beneficial but not required. Your workplace You will work in the Theoretical Physics Division: https://liu.se/organisation/liu/ifm/teofy . The division conducts research
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Do you like applying mathematical theories in practice to solve real-world challenges? Do you like working with top-notch, internationally recognized industrial partners? Would you like to push the
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Max Planck Institute for the Study of Crime, Security and Law, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 2 months ago
dedicated to innovative, theory-driven, and transparent research that adheres to Open Science standards. Your requirements A completed (or nearly completed) university degree (Master's or equivalent) in