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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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experimental data. • Proficiency in scientific programming and data analysis tools (e.g., Python, R, Linux/Unix environments). • Demonstrated track record of publishing scientific results in peer-reviewed
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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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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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: Good programming skills in C++ or Python Working knowledge of ROS Publication experience Who we are The Artificial Intelligence in Robotics (AiR) group, led by Dr Andriy Sarabakha, focuses
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analysis experience in analyzing and interpreting different types of ecological data skill in the creation and implementation of data management plans, as well as knowledge of different metadata standards
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research experience and competences within operations research, programming software, modelling, data science, etc. • Programming skills (Python, Java, Git, etc.) • Ability and interest in teamwork and