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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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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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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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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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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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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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, 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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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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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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-Doc will process large meteorological and emission datasets, prepare input data for the models, and contribute to scientific publications. Requirements: PhD degree in physics/mathematics