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on reinforcement learning (RL) for policy discovery in a multi-sector “integrated modeling environment” that connects fast ML metamodels of simulators (e.g., transport, energy, environment, climate events). The aim
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disassembly, reuse, and remanufacturing already at the early design stage. In this role, you will develop power converter integration concepts that support design for disassembly. Multi-physics simulation tools
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properties, e.g. high processability while maintaining an open porous structure. However, their fundamental vibrational behavior remains poorly understood. This limits the possibilities to enhance
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good process simulation and sustainability assessment skills. The FrameBio PhD project, is part of prestigious Marie Skłodowska-Curie Actions (MSCA) Doctoral Network, a collaboration between 16 partners
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complex interaction patterns that may carry important biological information. By integrating deep learning, genome-wide simulations, functional genomics, and large-scale biobank data, AI:GENOMIX aims
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. To do so, you will combine atomistic simulations (density functional theory and ab-initio molecular dynamics simulations) with new machine learning models to parameterize machine learning force fields
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will involve both computational predictions and experimental validation. The project will combine density functional theory calculations with machine learning and molecular dynamics simulations
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will: Develop robust control strategies that leverage semantic data structures for rapid deployment. Create workflows to bridge BIM, energy simulation engines (e.g., EnergyPlus, Modelica), and control
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communications, signal processing, or related fields at master’s level is essential. This includes a thorough understanding of the fundamentals, as well as modelling, simulation, and evaluation tools such as
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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments