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regulatory networks that are active in blood cells in health and disease. The successful candidates will have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field
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, bioinformatics, computer science, biostatistics, cancer genomics, or related fields. Candidates who are completing their PhD thesis are welcome to apply and should mention the expected date of their defense and
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Sign In Create Profile Postdoctoral Research Associate I - V (Center for Biomedical Informatics and Biostatistics (CB2)) Tucson, AZ, United States | req22753 Apply Now Share Save Job Posted: 5/7
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, and neuroscience. Minimum Qualifications MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications PhD in Bioinformatics, Biostatistics
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outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
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, with potential for renewal based on funding and performance. Additional Information: PhD in Molecular Biology, Biochemistry, Molecular Epidemiology, Evolutionary Biology, Bioinformatics, Biostatistics
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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University of California, Los Angeles | Los Angeles, California | United States | about 21 hours ago
diverse team of investigators at UCLA including experts in immunogenetics, immunology, transplantation, infectious disease, biostatistics, and computational immunology at the University of California Los
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. The position is open to applicants with a PhD in computational biology, bioinformatics, biostatistics, cancer genomics, network science, or related fields. The appointment is a fulltime position and is made
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large