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related field. You have a strong background in numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD specialists, so affinity with
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including transformers, self-supervised learning, foundation models, autoencoders or related architectures; strong programming skills in Python and experience with a deep-learning framework such as PyTorch
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architectures; • strong 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
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, for example using Python or comparable tools, and experience with GIS-based spatial analysis. The ability to work with heterogeneous geological, hydrogeological and monitoring datasets and to connect
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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related discipline; An interest in the impacts of land use; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of
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; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of an interdisciplinary research team. The following qualifications
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audiences and collaborate across disciplines; proficiency in spoken and written English. Experience with plant pathogens, microbiome sequencing, image-based phenotyping, R/Python, statistics or machine
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competence in quantitative and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior
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and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior exposure to firm-level