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Field
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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can
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with advanced photoreactors, modular LED light sources, inline analytics, and machine-learning algorithms for the autonomous optimization of photocatalytic reactions. Research will address key challenges
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical
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of Complex Systems team at the Institut des Sciences Analytiques (ISA), located in Villeurbanne on the campus of Université Claude Bernard Lyon 1. ISA is a research laboratory in analytical chemistry
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. Using an innovative multilevel approach, you will move beyond institutional averages to identify where inclusion succeeds, where disparities persist and how universities can create more equitable learning
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pharmacology, to better understand the complex interplay of the many factors that drive cardiometabolic disease. You can learn more in the Executive Summary of CBMR's Strategy 2024–2028 . CBMR was established in
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systems, data engineering, semantic interoperability, privacy-preserving data analytics, artificial intelligence or related fields. Your duties and responsibilities in this 4-year project include: analysing
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for digital patient twins in oncology and cardiovascular medicine. These models should learn patient representations across data types, organs, diseases, and time, capturing disease trajectory, prior history
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profiles. Identification of molecular signatures in platelet disorders and bleeding disorders of unknown cause (BDUC). Development of machine learning models for patient stratification. Integration