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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
Title Post-Doc Research Associate Vacancy ID PDS004724 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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-making. Among the key duties of this position are the following: Develop and refine computational models (e.g., mechanistic, machine learning, or hybrid approaches) for ICU patients. Integrate and analyze
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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discount programs Position Status Full Time Working Hours Standard Hours 37.50 Daily Work Shift Work Arrangement This position requires a fully on-site work arrangement. Union Description Post Docs - Regular
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Monday – Friday between the hours of 8:00 a.m. – 5:00 p.m. Additional hours may be needed during peak times. Job Summary The College of Public Health, Department of Biostatistics seeks a Post Doctor
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the development of high-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning
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Hours 37.50 Standard Hours 37.50 Daily Work Shift Day Work Arrangement This position requires a full onsite work arrangement. Union Description Post Docs - Regular Salaried Payroll Designation PeopleSoft
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management ”, funded by the EU Commission’s Horizon Europe Framework. The successful candidate will work on two integrated research agendas: Explore traditional and machine-learning based techniques in