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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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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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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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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 2 hours ago
Job Offer from November 03, 2025 The Max Planck Institute for Gravitational Physics (Albert Einstein Institute, AEI) in Potsdam, Germany, announces the opening of several PhD positions at the IMPRS
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interdisciplinary PhD projects in the Simulation and Data Lab Digital Bioeconomy. Each project combines natural sciences with computational and data-driven approaches, focusing on topics such as plant carbon
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Max Planck Institute for Demographic Research, Rostock | Rostock, Mecklenburg Vorpommern | Germany | 1 day ago
through the online application for the Studentship Programme, including: A copy of your Bachelor's academic transcript and certificate. A copy of your Master's academic transcript and certificate. If your
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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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, AI-enhanced simulations, and computational workflows to explore biological complexity and design solutions for a sustainable bioeconomy. The four PhD projects are: Further details on each project can
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techniques Writing and submitting computational proposals to obtain the computational resources required to accomplish the project Engage in the international simulation community Publishing the results
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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