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of Chemistry or Materials Science or Physics or related field Kinetic Monte Carlo simulations, quantum simulations, theoretical chemistry Excellent teamwork and communication skills in an interdisciplinary
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THE FIELDS OF: ATMOSPHERIC PHYSICS AND CHEMISTRY, ELECTROCHEMISTRY, ELECTROCHEMICAL ENERGY STORAGE (BATTERIES), ELECTRONICS, ELECTRICAL AND MECHANICAL ENGINEERING, HIGH-PERFORMANCE COMPUTING, MACHINE LEARNING
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mineral and metal-bearing raw materials more efficiently and to recycle them in an environmentally friendly way. The Group Geometallurgy and Particle Based Process Modelling is looking for a PhD Student (f
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, medical image reconstruction principles and experience with high-performance computer simulations in physics would be beneficial Experience in working with medical imaging data, especially with PET, and
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. Multiscale simulations of the downstream expansion behaviour of the engine exhaust plume for different atmospheric layers. Definition of suitable interfaces to process data for the climate models. Expected
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are offering four PhD positions in the Simulation and Data Lab Digital Bioeconomy (SDL-DBE). The SDL-DBE develops and applies multiscale models, AI-enhanced simulations, and computational workflows across IBG
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light harvesting to substrate/antibiotics transport through membrane pores. The Computational Physics and Biophysics Group is led by Prof. Ulrich Kleinekathöfer and is located at Constructor University
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or equivalent) in the field of aerospace engineering, physics or similar solid knowledge of Physical and Analytical Chemistry (phase changes), Computer Science and Informatics (Numerical Analysis; simulation
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simulation of reactive fluids, computational fluid dynamics) We particularly encourage applications from candidates with a computational background. What we offer Cutting-edge research in a dynamic work
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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