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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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batteries (RFB), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e., complex material layers that can be optimized to specific battery
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” funded by the VILLUM FONDEN. The overall aim of the project is to introduce microstructural engineering to the field of additive manufacturing (AM) of metals. This is to set the stage for optimizing metals
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Combustion Engines, ICE, have been optimized for fossil fuels for more than 140 years but the new fuels have properties that can enable a more efficient operation. Advanced combustion processes like HCCI, SACI
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be applying methods such as sensitivity analysis, robust optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and
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some background in one or more of the following areas: Mathematical Optimization / Operations Research Reinforcement Learning, Machine Learning, and/or Multi-agent systems Game Theory Algorithms