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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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(mechanical, electromagnetic, and, possibly, fluid-dynamic) and multi-scale simulations for drone components using finite element tools (e.g., Abaqus, Comsol). · Supporting the development of an AI
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enhanced MRI with computer simulations of image contrast and mass spectrometric imaging of tissue samples and single cells. This project is part of the Collaborative Research Centre 1450 “Insight
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like to more widely explore the possibility of computer simulations to refine the targeted synthesis even more and predict the self-assembly even better. Who we are · The Research Training Group RTG2670
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copy of the relevant diplomas showing marks, indicating the grading system List of publications, if applicable Name and addresses of at least two referees Early application is highly encouraged, as the
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Curriculum Vitae • An example of your scientific writing in English (e.g. paper, report, thesis) • Copy of Bachelor’s degree and overview of grades (e.g. ‘Transcript of Records’) • Copy of Master’s degree and
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documents in PDF format: 1. Motivated letter of application (max. one page) 2. CV incl. education, experience, language skills and other skills relevant for the position 3. Certified copy of original Master
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alternative CO2 concentration trajectories will result in altered ecosystem dynamics and resilience. O3: Evaluate the ecosystem resilience simulated by a vegetation demography model with emerging signals
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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neurodegenerative diseases. The most promising candidates will be assessed using advanced docking and dynamics simulation techniques, providing insights into their potential interactions. To further substantiate