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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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ML models and Python programming. Work Objectives: The main objective of this position is to develop, implement, and validate advanced machine learning methodologies within the scope of the project
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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role in defining system requirements and developing a robust AI framework to model and anticipate opponent behaviours and beliefs, leveraging state-of-the-art methods in machine learning and
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engineering/M2) to have a solid background in applied mathematics, Machine/Deep Learning, in particular generative models (diffusion models, flow matching), as well as in statistical signal/image processing and
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optimal transport and gradient flows to machine learning and optimization applications, such as deep generative models, sampling, inference, stochastic optimization, and beyond. The doctoral student will
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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research background in or research experience with one or more of the following topics: Natural language processing & language modeling Machine learning & representation learning Interpretability and
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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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explainable AI techniques, machine learning, Large Language Models, case-based reasoning, and ontologies. Specific Requirements Programming in python, XAI libraries, machine learning and deep learning libraries