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on precision therapeutics for children living with Inflammatory Bowel Disease (IBD). We integrate clinical pharmacology, -omics methods and novel endpoints into innovative applications that lead to better
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Sciences, Geodesy, Physics, Mathematics, Computer Sciences, Civil & Environmental Engineering, or a related field. PhD awarded no more than four years prior to the effective date of appointment with a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 30 days ago
Carolina, US Hiring Range $63,480 - $77,076 Proposed Start Date Estimated Duration of Appointment 24 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 18-Jul-26 Location: East Lansing, Michigan Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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structures. Development of textiles as test instruments. Textile characterisation and studies of actuation. To participate in clinical studies and analyse the outcome through biomarkers, pain estimation scales
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and machine-learning methods for rapid surrogate modeling, model calibration, state estimation, uncertainty quantification, and physics-informed prediction. Contribute to U.S. Department of Energy
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of an extension, subject to funding. You will apply and develop cutting-edge machine learning methods to integrate and analyse multi-omic data to identify disease phenotypes. A key aspect of the role is to bridge
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skills, especially in quantitative and/or mixed methods. Experience with statistical analysis including cluster analysis Comprehensive computer skills, with the ability to learn and utilize new and
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research and development in the areas of gauge field generation, linear and eigen-solvers, or novel analysis methods. This position will reside in the Advanced Computing for Nuclear, Particle and
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering