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Postdoc (f/m/d) in Irradiation Tolerance of Additive Manufactured Ferritic/Martensitic Steels for...
damage, and mechanical properties # Literature research, data analysis, and development of scientific concepts # Coordination of supplementary mechanical testing (e.g. fracture mechanics, small punch tests
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division 8.5 Planning, performing, and evaluating in-situ/4D computed tomography experiments Developing software for the quantitative evaluation of various image data sets (algorithms for detecting volume
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engineering, or a related discipline, with a clear and demonstrated focus on diabetes research. Experience with iPSC models and data analysis: Hands-on experience with induced pluripotent stem cell (iPSC
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, computational data analysis and microbial evolution, with a solid understanding of quantitative statistics and programming (Python, R, MATLAB, etc.). Additional expertise in ancient DNA, (bio)archaeology
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assessment of materials using microbial communities Processing and analysis of high-throughput sequence data (DNA, RNA, community data, whole genome data, data mining) Data management for the division
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characterization of catalytic reaction intermediates and their transitions. Single particle cryo-EM sample preparation and data collection using state-of-the-art equipment at our core facility, density determination
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contact information for three references to jennifer.li@tuebingen.mpg.de with the subject line: NEUROENGINEER-APP.
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are welcome. Our recruiting is decentralized - your application will be reviewed directly by the specialist department in which you could work in the future. More information about our application process
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in peer-reviewed journals Requirements Excellent doctoral degree in neuroscience or biophysics Experience in molecular biology, biochemistry, and electrophysiology; data science skills are also
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Computational Biomolecular Dynamics Prof. Dr. Bert de Groot Am Faßberg 11 37077 Göttingen www.mpinat.mpg.de/degroot Information pursuant to Article 13 DSGVO on the collection and processing of personal data