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and assembly of concrete PCMs. RecyWax+ offers a PhD position for molecular-dynamics simulations of novel PCM systems for thermal storage. Job Description Recywax+ is supported within the Dutch Research
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Division of NORCE Norwegian Research Centre there is a 3-year full time PhD research fellow position available within the field of modeling and simulation of CO2 storage. The position is for a fixed-term
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allowed computational fluid dynamics (CFD) to flourish, becoming an indispensable for many industries. Simulating the full Navier-Stokes equations is computationally prohibitive for most applications, so
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engineering wake models to mesoscale simulations with wind-farm parametrisation, when predicting wake effects and thus annual energy production. The student will improve current models used by industry such as
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of an external magnetic field into the phase-field model and to simulate the microstructural evolution for different cooling rates. - Phase-field modeling of the phase transition in the Fe-Ni alloy. The selected
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provide different opportunities for you to develop. You would gain experience across a wide range of interdisciplinary areas including nano-assembly, optics, colloidal chemistry, and simulations across
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for digital and hybrid analog/digital beamforming in multiantenna, multicarrier settings involving joint sensing and communication. Simulation and performance evaluation. Where to apply Website https
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: Professional experience in universities and companies in the field of numerical simulation of heat treatments (punctuation: 20) Item 03: Proven expertise in CFD, particularly in steel heat treatment issues and
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Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys ). The PhD position is focused on efficient algorithms for the simulation of non-adiabatic exciton transfer dynamics in light
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning