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on the topic assigned for each position. Requirements: outstanding university degree (typically M. Sc.) in Computer Science, Data Science, Statistics, Mathematics or another relevant field study with good GPA
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Sustainable Decision Making (Prof. Dr. Clemens Thielen), which is located at the TUM Campus Straubing for Biotechnology and Sustainability (TUMCS) and affiliated with the Department of Mathematics. The expected
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Parkinson’s Disease, or markers of brain structure and functioning, depending on the dataset. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in
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knowledge in plant species identification. The successful candidate should be able to independently conduct statistical analyses in R and hold a valid driver's license. Experience in GIS and handling large
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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data
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Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly
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: TRR408-A7 Investigators: Prof. Dr. Ostap Okhrin, Chair of Econometrics and Statistics esp. in the Transport Sector and co-supervised by Prof. Dr. Kai Nagel, Chair of Transportation System
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received after the review date will only be considered if the position has not yet been filled. Position description The Computational Medicine Research Group led by Prof. Pratik Shah at the University
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demonstrable experience in academic writing for publication, e.g. peer reviewed paper(s) and/or report(s) Advanced statistical software skills are desirable, e.g., regression analyses, repeated-measures analysis