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applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc.. Participation in these projects will include
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requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University of Oslo, Department of Mathematics PhD programme
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, starting date, and project-specific qualification requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 1 day ago
innovation by advancing regulatory science, providing industry with predictable, consistent, transparent, and efficient regulatory pathways, and assuring consumer confidence in devices marketed in the U.S
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 1 day ago
understandable and accessible science-based information about the products overseen by CDRH. CDRH facilitates medical device innovation by advancing regulatory science, providing industry with predictable
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 1 day ago
regulatory science, providing industry with predictable, consistent, transparent, and efficient regulatory pathways, and assuring consumer confidence in devices marketed in the U.S. Research Project: CDRH’s
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integration and technology selection. Participating in aircraft operations trade studies to evaluate economic viability and military effectiveness. Developing and applying analytical skills to predict installed
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory
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limited to: Computational and applied mathematics; artificial intelligence for sciences (especially biomedical science, brain science, and neuroscience); data science and machine learning; modeling