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Field
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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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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research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong communication skills in English and good knowledge of French
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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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interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms
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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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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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. 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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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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resonance spectroscopy, imaging (MRI), Applied Mathematics or Machine learning. We are looking for talented, highly-motivated experimentally skilled young scientists with Master degrees or equivalent or PhD