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applicants interested in developing a research program in the field of cancer biology are welcomed to apply. Expertise in the fields of molecular biology, cell biology, and genetics are expected. Experienced
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for data analysis and research. Machine Learning Skills: Experience with machine learning algorithms, transformers, or large language models to analyze genomic data. Computational Proficiency: Skilled in R
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research program sponsored by federal agencies such as the U.S. Department of Defense and the National Science Foundation (NSF). The focus of the research is on developing innovative physics-informed deep
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disorders. Communicate findings to the scientific community through conference presentations and publications. Preferred Qualifications: PhD in Computational Biology, Bioinformatics, Genetics, or a related
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Center. The broad areas of interest in Dr. Trinh's lab include: gene regulation by RNAs and proteins, modulation of gene-specific chromatin architecture, blood development, cancer and genetic diseases
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on studying Cancer Biology and Stem Cell Biology in the laboratory of Dr. Lu Le , Professor and Chair of the Department of Dermatology. Dr. Le's lab investigates how genetic and microenvironmental factors
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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through the implementation of the Coping Power Rural Program , Double Check Online program, Integrative Data Analysis Projects, the mental health screening of students, and related evidence-based programs
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Collaborate with leading researchers within and outside UVA Qualifications: U.S. citizenship required Ph.D. in Data Science, Statistics, Computer Science, Network Science, Physics, Engineering, Sociology
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recruitment and buy-in, CARE program implementation and fidelity of implementation monitoring, and data analysis and manuscript generation. This position will complete duties as follows: Recruiting: Reaching