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the Reinhart-Koselleck programme for innovative high risk-high gain research. Requirements: university degree in chemistry or physics and profound knowledge in computational and theoretical physics/chemistry
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. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods
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materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH (AMO ) in Aachen, Forschungszentrum Jülich (FZJ ), Max Planck
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interested in working at the boundaries of several research domains Master's degree in computational biology, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline
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Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, Computer Engineering, Genetics/Genomics or related field experience with ‘omics platform output experience with biological datasets
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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letter, transcript of records of the master program degree, a summary of your master thesis (if already completed) and the names (affiliation, telephone, e-mail) of two references) by October 10, 2025
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“Light- versus electron-induced spin-state switching of complexes on insulating layers” within the Priority Programme SPP 2491 “Interactive Spin-State Switching” This DFG-funded project aims
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably