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skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods
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until the positions are filled. Contact: Dr. ir. Gerben R. Stouten Environmental Biotechnology, Department of Geosciences, University of Tübingen
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to apply E-mail bewerbung@gsi.de Website https://www.gsi.de/en/jobscareer/job_offers/job_offers/postdoctoral-scientist-r… Requirements Additional Information Website for additional job details https
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datasets (SQL, Python, R) Develop reproducible data pipelines and perform statistical analyses Visualize trends and outcomes in patient data Collaborate closely with clinicians, statisticians, and IT
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-envbiotechspam prevention@ifg.uni-tuebingen.de . Applications are reviewed until the positions are filled. Contact: Dr. ir. Gerben R. Stouten Environmental Biotechnology, Department of Geosciences, University
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; alternatively, R and/or Python). Strong written and oral communication skills in German and English (both at least B2 level, preferably C1 level) Very good organizational skills, a proactive hands-on approach
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with experience in causal inference using panel data (e.g., Difference-in-differences) solid knowledge of Stata, R and/or Python for conducting econometric analysis previous knowledge of conducting
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-domain context. Good knowledge of programming languages (e.g. Python, R, Java, C#, C++) Good knowledge of object orientation and at least basic knowledge of UML Knowledge in the field of machine learning
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for replication timing and directionality, GRO-seq for nascent transcription, and DRIP-seq for R-loop detection. These methods allow us to integrate replication, transcription, and DNA repair features at nucleotide
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handling quantitative data and extensive experience with at least one statistical software package (preferably R or Stata) Ability to conduct literature searches and prepare concise summaries of scientific