267 assistant-professor-computer-science-data-"Meta" Postdoctoral positions in Germany
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, establishing new methods Writing publications on the topic and presenting at international conferences and meetings Your profile: Completed doctorate in natural sciences and enjoyment of basic and translational
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using experimental data from laboratory setups Validate models through targeted measurements and structured testing procedures Analyse loss mechanisms and system efficiencies under various operating
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infrastructure. The position entails active involvement in both experimental procedures and computational data analysis, alongside the improvement of advanced methodologies, including single-cell proteomics
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part of the School of Computation, Information and Technology (CIT) of TUM. The position is for 2 years and follows state regulations in accordance with the Collective Agreement for the Public Service
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master's or diploma degree in computer science or mathematics already successfully completed doctorate confident demeanor and proficient use of German and English (both equivalent to at least B2) expertise
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models, multi-omics analyses (e.g., RNA-seq, ATAC-seq, scRNA-seq, scDNA-seq, WES/WGS, targeted NGS, DigiWest), a broad spectrum of molecular and cellular biology techniques, biochemistry and biophysics
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Postdoctoral Research Associate in Sum-Frequency Generation Microscopy of Biomolecular Self-Assembly
the south-west of Berlin, more than 400 employees across 5 departments at the FHI conduct and facilitate top-level research in fundamental Physics and Chemistry. The Department of Physical Chemistry in
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of epithelial plasticity in kidney repair to contribute to the preparation of scientific publications, grant applications and presentations to contribute to teaching in Biomedical Sciences (Master programme
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, RNA seq, single cell sequencing, spatial transcriptomics), bioinformatics tools and human studies. Your profile: PhD in bioinformatics, oncology or computational biology profound experience in system
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human