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RNA sequencing workflows and basic bioinformatics • Experience with Python and R for transcriptomic data analysis We offer The position (TV-L13 (65%) salary according to German public sector regulations
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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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-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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corresponding projects Your Profile: Master’s degree in computer science, bio/-cheminformatics or natural sciences Strong programming skills (Python, R, Java, …) and interest to work in polyglot software
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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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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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(e.g. Python, R) or a universal programming language (e.g. C#, Java, C++, Rust) Knowledge of the use of relational and non-relational databases Very good English skills (written and spoken) High
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related fields Native speaker or excellent command of German and English Experience in programming (in R) or motivation to learn and perform bioinformatic analyses of bacterial DNA (16S rRNA and shotgun
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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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is not essential. The successful candidate should be acquainted with relevant software, such as STATA, R, TreeAge and Matlab. We are offering The position will be based in Kiel and is initially funded