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Field
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related field. Demonstrable expertise in foundation models / large language models, multimodal modelling, or agentic / multi-agent systems is essential. Experience or strong knowledge of causal discovery
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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model's development. The position is based at LOCEAN on the Pierre and Marie Curie campus of Sorbonne University. The LOCEAN laboratory (https://locean-ipsl.upmc.fr ) is one of nine laboratories in
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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. Knowledge in statistical modeling, data science, exploratory analysis, and natural language processing applied to legal texts and empirical legal studies is highly desirable. Candidates must have excellent
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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knowledge with data-driven methods. Physical knowledge includes, among others, kinematics and the dynamics of the robot, terrain interaction and contact models, and environmental physics, such as wind. The
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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by Dr. Tim Pleskac (cognitive and decision modeling) and Dr. David Crandall (computer vision and AI). The postdoc will lead the development, integration, and testing of computational models of decision
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Knowledge of MLOps concepts, including dataset ops, training pipelines, evaluation frameworks, deployment and monitoring. Understanding of deep learning fundamentals and hands-on experience with model