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-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment offering varied learning
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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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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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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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, molecular profiling, and transgenesis, and is passionate about learning systems neuroscience and all-optical physiological approaches to the study of brain-body interactions. Our mentorship goal
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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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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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. We are seeking highly motivated individuals who are passionate about science, learning and having fun. Candidates with a strong background in molecular and cellular biology, and/or experience in
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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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clinical or behavioral research, particularly with children and families Motivation to learn state-of-the-art methods and approaches for clinical trials Enthusiasm to improve the health of children and