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, lipidomics, and proteomics. Experience with multi-omics analyses is an advantage; Basic proficiency in data analysis using R, Python, and/or Matlab, with a strong interest in computational methods
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numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD specialists, so affinity with these techniques is a plus but not required. Affinity with
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, Earth Sciences, Civil Engineering, or a related field. You have a strong background in numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD
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programming skills in Python and experience with a deep-learning framework such as PyTorch, including training and evaluating models on GPU/HPC infrastructure; experience working with high-dimensional
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assessment. Programming and data-analysis skills in a reproducible scientific workflow, for example using Python or comparable tools, and experience with GIS-based spatial analysis. The ability to work
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, image-based phenotyping, R/Python, statistics or machine learning is an advantage. We do not expect candidates to already have expertise in all of these areas. Our offer a position for 18 months, with
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), Python, or Stata. Prior exposure to firm-level databases such as Orbis is an advantage; have demonstrable analytical and project management skills; have an excellent command of written and spoken English
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case-study research; Strong skills in building and managing complex relational datasets; Proficiency in Python, R or comparable analytical languages and software; Proficiency in the use of large-language
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numerical groundwater modelling, including the use of coding (e.g. Python) and incorporation of geohydrological schematizations; The ability to work independently and take initiative, with good
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skills in Python, R, or similar languages. Self-starting and collaborative attitude. Preferred qualifications and experience AI/ML applications: applying machine learning or AI to integrate, discover