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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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, circular economy and green transition. You will specifically contribute to European research projects on blue biomass valorization via different technologies for micro and macro algae production and
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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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programming in Python and the modern AI ecosystem, including PyTorch, HuggingFace, and ONNX and you are comfortable working systematically with complex technical problems. Experience with industrial
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solid understanding of electromagnetic theory, microwave principles, and computational electromagnetics. You are comfortable working with full-wave simulation techniques such as finite-difference time
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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