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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program
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new technologies related mainly to the Internet of Things, the use of large amounts of data, and the autonomy of systems due to the development of AI. The effect can be beneficial, for example by
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3 Dec 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Physics Chemistry Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country
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expertise in Neuropixels or other large-scale in vivo electrophysiology techniques. An expert neural data analyst may be considered even with minimal animal experience. Required Application Materials: Cover
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belongs to one of the three marine stations of Sorbonne Université. With about 120 permanent staff, the LOV generates and analyses a large quantity of marine data, including imaging, genomic, and satellite
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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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are looking for a candidate with a PhD in epidemiology, biostatistics, data science, medical science, or a related field, with solid statistical skills and the ability to independently manage data from multiple
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equivalent to PhD level in health data science or similar field. Experience working within multidisciplinary teams, in medical statistics and in the analysis of large healthcare datasets (such as CPRD
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to analyze human gut microbiome data from large cohorts. Responsibilities Microbe-microbe and host microbe interaction analysis in multiple cohorts (n=2500) Evaluate differential fitness of specific strains in
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) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive inverse problems. The team aims at developing