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or empirical research including statistical data analysis. The position is associated with the project “Probabilistic Reasoning about Common Ground” under the auspices of the Collaborative Research Center
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rigorous data acquisition and analysis. Write scientific reports, manuscripts, and contribute to grant proposals. Participate actively in group meetings, present your research, and contribute intellectually
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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, paleobiology, geoecology, evolutionary research, or a related field Experience in computational modeling, analysis of ecological or evolutionary data, or causal inference Proficiency in model calibration
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, bright individuals with a diploma or master’s degree in bioinformatics, biochemistry, biology, molecular/translational medicine or related subjects and a PhD in life and computer sciences. Previous
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. Publish and present findings in international journals and conferences. Supervise and mentor junior researchers and students. Profile PhD in Computer Science, Biomedical Engineering, Data Science, or a
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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | 30 days ago
conducted both in and outside the lab. Conduct data acquisition in diverse real-world contexts and with diverse participant groups, including underrepresented populations. Perform advanced data analysis
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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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online surveys Experience in qualitative document analysis Experience in management of international scholarly research projects Experience in supervision of undergraduate and PhD students We offer
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approaches for the analysis of omics, digital, and molecular pathology data, to support precision medicine. We work with real-world data in a highly interdisciplinary environment fostering translational