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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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, 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