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                Field
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                advantageous but not mandatory: Simulation sciences, ideally modelling microswimmers, Wet-Lab work, Different forms of microscopy Our Offer: We work on the very latest issues that impact our society and are 
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                transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies, AI-enhanced simulations, and computational workflows to explore biological 
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                Your Job: You will work as a doctoral researcher within one of four interdisciplinary PhD projects in the Simulation and Data Lab Digital Bioeconomy. Each project combines natural sciences with 
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                Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning 
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                future scenario simulation of VBD Including machine learning, statistical, and process-based models Present findings at scientific conferences and publish in peer-reviewed journals Contribute 
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                and materials simulations at the institutes PGI-12 and IET-3. Thereby, Forschungszentrum Jülich will provide you with an ideal environment for your project with unique possibilities to access the latest 
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                , you will benefit from the combined expertise in quantum algorithms and materials simulations at the institutes PGI-12 and IET-3. Thereby, Forschungszentrum Jülich will provide you with an ideal 
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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 
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                future scenario simulation of VBD. Including machine learning, statistical, and process-based models. Present findings at scientific conferences and publish in peer-reviewed journals. Contribute 
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                -driven simulations, optical remote sensing and biogeochemical modeling to predict seagrass distribution under various climate and nutrient scenarios. SEAGUARD aims to provide science-based recommendations