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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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-on experimentation with advanced digital fabrication, numerical modelling, material testing, and process optimization. You will work on the fabrication and mechanical characterization of composite specimens with
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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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states of light, atoms, material objects, and their use for quantum sensing, quantum communication and quantum simulations are the core activities of the group. CBQS is a collaborative effort between the
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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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, processing video data, and applying AI models to improve efficiency in population and behavioral analyses. Fieldwork will be combined with statistical and spatial analysis using RStudio and GIS tools. In
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surgical robots across various surgical applications, using techniques such as advanced sensing, AI-based and reinforcement learning (RL)-based control, and soft continuum robot simulation. The starting date
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and reinforcement learning (RL)-based control, and soft continuum robot simulation. The starting date is expected to be February 15, 2025, or as soon as possible thereafter and will be agreed with
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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