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Field
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combines machine learning, legal applications, and empirical evaluation in collaboration with judicial partners. The project offers a unique opportunity to work on real-world, high-stakes AI systems in
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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expertise. 2. Curriculum Vitae including a list of publications (maximum 3 pages). Where to apply Website https://jobrxiv.org/job/phd-position-in-machine-learning-and-ecology/?utm_sourc… Requirements
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samples, phase retrieval in this regime remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation
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meters. These instruments produce large amounts of data that require several processing steps before the relevant physical variables are obtained. Typically, machine learning methods are used to optimize
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09928 Requirements Skills/Qualifications Profile: - The candidate is completing a Master's or engineering’s degree in Computer Vision, Electrical
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modelling, analysis of complex dynamical systems, simulation, analysis of large-scale datasets with machine learning methods, and software development are beneficial Good organisational skills and ability
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. The project is highly interdisciplinary and will provide training in clinical microbiology, infection epidemiology, machine learning, data harmonization, and data science. You will also participate in DDLS