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analytics Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical datasets Collaborating closely with data providers, clinicians, and technical teams
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Machine Learning for Neutron
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-funded LEARN project uses high-quality, cross-national data and advanced analytical techniques to investigate the key processes through which major disruptive events affect children’s educational
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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Agriculture: Natural Language Interfaces over Robotic and Analytical Farming Systems In the context of the MSCA JD project GreenFieldData https://www.eu4greenfielddata.eu/ GreenFieldData: IoRT Data Management
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Package 6), equipping them with advanced skills in reservoir modeling, machine learning, advanced oxidation processes (AOP), and microbial enhanced recovery. DCs will also develop intuitive fluid chemistry
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machine learning e.g., GNNs, graph representation learning, hierarchical or probabilistic graph models Strong analytical and problem‑solving abilities, with enthusiasm for developing novel computational
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Technologies de l'Information et de la Communication Field: Telecommunications / Machine Learning / Statistical Signal Processing. Research Lab: L2S (Laboratoire des Signaux et Systèmes) Advisor: Antoine BERTHET