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disciplines, including biomathematics, biostatistics, and molecular biology. The candidate is expected to have a solid grounding in programming in R, Python, and mathematics/statistics.The main duties involved
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Biostatistics & Bioinformatics Duke University invites applications for a postdoctoral associate in scientific machine learning (SciML) as part of a new NIH-funded Center for Excellence in Multiscale Immune
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immunologic skin diseases. Candidates are welcome from various interrelated backgrounds, such as epidemiology, computer science, public health, health services research/health policy, and/or biostatistics
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addition, the project includes collaborations with researchers in other disciplines, including biomathematics, biostatistics, and molecular biology. The candidate is expected to have a solid grounding in
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discipline (e.g. Medical Informatics, Computer Science, Data Science, Computational Social Science, Epidemiology, Biostatistics, etc.) Strong research track record, programming, and implementation skills
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the scope of microbial ecology, environmental microbiology, and bioremediation. Strong expertise in biostatistical analysis and molecular biology is highly required. The postoctorate will perform experiments
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. Requirements Essential Requirements PhD Degree in a relevant scientific field (e.g. genetic epidemiology, molecular epidemiology, statistical genetics, biostatistics, health data science, etc) to be obtained max
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requested and appropriate. Requirements Essential Requirements PhD Degree in a relevant scientific field (e.g. genetic epidemiology, molecular epidemiology, statistical genetics, biostatistics, health
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researcher with a strong interest in genetic and cancer epidemiology research. The applicant must have a Ph.D. (or equivalent qualification) with training in epidemiology, biostatistics, and/or statistical
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internet connection for duties undertaken remotely. Qualifications Required Qualifications: A PhD degree in Biostatistics, Statistics, Computer Science or a related field who possess STRONG computing