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- Participatory Methods for Impact chain assessment and backcasting Reference code: 50139757_2 Commencement date: August 1st, 2025 Work location: Hamburg Application deadline: 04.06.2025 The Climate Service Center
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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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. 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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The position is funded for 3 years (65% TV-L E13), starting on January 1st, 2026, within the framework of the TTU HIV program of the German Center for Infection Research (DZIF). Project Description: Although
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models on edge devices Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science
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computational biology, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline Knowledge and experience in the analysis of metagenomics and/or biological high-throughput
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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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for biological image data, scalable computer vision methods preparation of publications for submission to top-tier machine learning conferences contribution to teaching and/or supervision of Bachelor's and
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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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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