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degree in Stastistical, Mathematics Required Knowledge : Solid background in statistics or Machine Learning, Proficiency in classical statistical methods in health research (e.g., logistic regression
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of and experience with hydrological and/or hydrogeological modeling - Knowledge of and experience with AI concepts (machine learning, deep learning, and PINNS) and/or digital twin development - Experience
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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
. This dataset will enable the training of specialized deep learning models (neural network or transformer) for automated segmentation of tibial plateau fractures. iii) The algorithm must then be trained
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. The candidate must be able to communicate in English (oral and written). The knowledge of the French language is not required. The candidate must have a strong interest in machine learning. Skills in
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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to candidates from a broad range of AI subfields, including, but not limited to machine learning, generative AI, computer vision, representation and reasoning, natural language processing
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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing