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. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process & microstructure with
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applications such as in-situ resource utilization (ISRU) or construction in reduced-gravity environments. Experience with simulation tools for thermal, mechanical, or flow processes (e.g., COMSOL, ANSYS
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and qualifications. Possible topics are: The theory of ultrafast pump-probe experiments (e.g., time-resolved X-ray scattering and spectroscopy), simulations and data processing of actual ultrafast
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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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PhD scholarship in Corrosion Mechanisms of Power Semiconductor Device and Components - DTU Construct
, gases and applied potential conditions. The project will also include the development of advanced simulation models to characterize and predict moisture transport through gel substrate and interfacial
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installation at DTU Campus for server room cooling will serve as a pilot for testing, control, and optimization of such latent thermal energy storage. This installation will be taken into operation in March 2026
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materials Development of simulation models for the electromagnetic wave interaction analysis Machine learning design of antennas and metasurfaces for interaction with lossy materials Research into practical
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of such latent thermal energy storage. This installation will be taken into operation in March 2026. To optimize the controllability of cold storage using phase change materials, a novel charge determination
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. Application procedure Your complete online application must be submitted no later than 12 October 2025 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop