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of in vitro and in vivo pre-clinical models, including hiPSC-derived systems The postdoctoral project will combine experimental (wet-lab) and computational (dry lab) approaches Be part of Geman Center
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on the influence of Alzheimer’s disease and aging on changes in cognitive functions in humans. The project combines cutting-edge technologies from genetics, proteomics and statistical modeling to understand
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, Computational Social Science, or a closely related discipline Demonstrated expertise in research on anti-democratic attitudes, political extremism, and populist ideologies, supported by relevant publications
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Max Planck Institute for Plasma Physics (Garching), Garching | Garching an der Alz, Bayern | Germany | about 2 months ago
the experimental data with support of simulations Present the results in meetings, conferences and reports Active contribution to the experimental program to obtain beam properties Strong
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such as Git. Expertise in at least one of the following areas: Computer vision (e.g., object detection, segmentation, classification, explainable AI) Microbiome data analysis, bioinformatics, or modeling
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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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topic from the areas represented within the group. The group’s interdisciplinary focus includes not only classical topics in numerical analysis, such as the analysis of nonlinear PDEs, but also modeling
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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genomic approaches Application of the modeling approaches in relevant downstream tasks Co-development of high-performance computing AI training codes for the first European Exascale Supercomputer JUPITER