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these methods across different crops to identify conserved patterns of stress resilience 4. Identify candidate genes associated with key agronomic traits related to resilience 5. Contribute to software and web
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and publication. Microbiome research and host-associated microbial communities. The ideal
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and students with a background in a number of different disciplines, including biology, molecular biology, statistics, chemistry, and computer science. About the research project We are seeking a
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environments. Experience with software such as R, Python, SPSS, Stata, Sawtooth, Qualtrics or similar tools will be considered an advantage. The successful candidate should have strong analytical skills, good
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environments, scripting/programming (R, Python and Bash), high-performance computing and development of reproducible analysis pipelines. Demonstrated ability to independently develop, modify and troubleshoot
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scripting (R, Python) and programming. Experience with High-Performance Computing (HPC) environments and the management/analysis of large datasets. The ability to communicate effectively in English (both
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this call will take on leading roles in the empirical work of the project, with different but complementary profiles. Both postdocs are expected to have an affinity to computational methods and tools
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pipelines for 16s rRNA and WGS sequence data from pathogens. Advanced computer programming skills in R and Python and preferably also other languages and demonstrated proficiency in UNIX, shell scripting