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our system as well as for probe design to allow different data operations such as random access and deletion in the system. If you are interested in helping us to create the new generation of molecular
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, biosignals) • Statistical modeling and dimensionality reduction • Python-based scientific computing (NumPy, SciPy, PyTorch/JAX, etc.) Core Research Skills • Privacy and/or sense-of-agency ethical analysis
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Postdoc in Decoding Biological Nitrification Inhibition (BNI) in Cereals: Integrating Metabolomic...
An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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programming language (e.g., R, Python) Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil
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An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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programming language (e.g., R, Python) Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil
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• Experience with data science and biodiversity informatics, in particular handling of scientific collection data • Solid knowledge of R, python and/or similar programming languages • Experience with
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, components, overall system performance) Numerical methods and simulation tools (e.g., Python/Matlab, CFD modelling, optimization) Beyond technical skills, we value people who contribute to a healthy and
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AgriLife and make a difference in the world! Position Information: The successful candidate will contribute to developing and evaluating terrestrial and aquatic nitrogen dynamics and emission modeling, with
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bioinformatic WGS data analysis and interpretation from bacterial isolates for AMR surveillance and/or genomic epidemiology. Proficiency in relevant bioinformatics tools and programming languages (e.g., Python, R