42 modelling-and-simulation-of-combustion-postdoc PhD positions at Technical University of Denmark in Denmark
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measurement techniques/ sensors. Experience with system modelling and simulation (e.g., TRNSYS, Python, or similar tools). System and control engineering (e.g. digital twins, model predictive control) –pre
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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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materials/ Latent thermal energy storage is an advantage. Hands-on experience with experimental setups and measurement techniques/ sensors. Experience with system modelling and simulation (e.g., TRNSYS
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of an ambitious and team-based research environment with around 50 international staff members, postdocs, and PhD students. Responsibilities and qualifications The successful candidates will contribute
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well organized, structured, self-driven, and enjoy interacting and collaborating with colleagues, including PhD students and postdocs. You are also expected to take part in the supervision of BSc and MSc
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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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, 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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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
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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 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