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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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research-practice partnerships and collaborations with community organizations. These partnerships provide fellows with opportunities to learn to collaborate with practitioners and policymakers to identify
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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, EBV, HBV, HPV) and chemical biology trainees with a willingness and aptitude for learning new experimental systems. Our projects require an adaptable mix of molecular virology as well as protein
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position to join NIH-funded projects in collaboration with the U.S. Census Bureau's Enhancing Health Data (EHealth) Program that develop new integrated data to improve our understanding of the socio-economic
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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, statistical analysis of clinical trials, and all aspects of project leadership. This position will also include collaboration and supervision of project staff. Required Qualifications: Ph.D. or equivalent in a
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for Human and Planetary Health, an interdisciplinary Center at the Woods Institute for the Environment in close collaboration with the Center for Innovation in Global Health at the School of Medicine
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to foster translational research that bridges discoveries from bench to bedside and back, with the ultimate goal of improving patient care for all older adults, through collaboration, mentorship, and