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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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tracking, pupillometry or related psychophysiological methods, or is eager to acquire these techniques; has strong quantitative and statistical skills (preferably using R and/or Python); demonstrates
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building and managing complex relational datasets; Proficiency in Python, R or comparable analytical languages and software; Proficiency in the use of large-language models (LLMs) for quantitative and/or