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of possible methodological components include self-supervised temporal representation learning for large volumes of unlabeled AE/electrochemical time-series data, switching state-space models that describe
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data
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, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information. By developing
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expertise in research and development in the following areas of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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Mattelaer, Christophe Ringeval). Research activities in include SM and BSM aspects of collider physics (LHC and future colliders, simulation tools, machine learning, effective field theories, amplitude
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 18 days ago
disseminate the developed methods. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09574 Requirements Skills/Qualifications PhD in Computer Science, Machine Learning, Bioinformatics
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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their