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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
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assess rank and identify the most promising fields for cyclic hydrogen storage such that they can be assessed in more detail using traditional reservoir characterisation, modelling, and simulation
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representation of real-world marine structures and their corrosion processes. By integrating comprehensive sensor measurements, experimental data on corrosion, and advanced predictive modelling (including physics
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Challenge: Understanding how micromobility affects pedestrian stress and comfort Change: Leveraging XR simulations to explore real-world pedestrian interactions. Impact: Guiding urban policy toward
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simulations including heat pumps, buffer storage, and building demand. Using monitoring data from the experimental façade at TU Delft’s Green Village to calibrate and validate models. Investigating control
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. Strong background in modeling and simulation of thermal/energy systems, preferably including PV/T or hybrid solar systems. Experience with numerical modeling (e.g. MATLAB/Simulink, Modelica, COMSOL
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options for sports and cultural activities . You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal
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experience in experimental durability research of construction materials; Solid background in numerical simulation and multi-scale modeling; Strong publication record in relevant journals; Excellent written
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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simulators. In this position, you will be encouraged to identify and tackle important challenges in quantum simulators, taking advantage of existing collaborations with leading experimental groups. You may
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conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement learning, to accelerate CFD optimization and enable adaptive control