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Field
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in the computational modeling of infectious diseases. Because this position focuses on the development of the individual in all aspects of research and mentorship, and because of Lehigh University’s
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and systemic health using an evolutionary health framework. There will be a specific emphasis on applying advanced bioinformatics and statistical approaches to large, population-level datasets in
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, mathematical and computational sciences. The ultimate goal of iTHEMS is to unravel the mystery of the Universe, matter, and life, as well as to solve key problems in modern society through interdisciplinary
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on computers, standing, lifting 10 lbs. A well-qualified candidate for this position will also possess: PhD in Biology, Microbiology, Molecular Biology, Biochemistry. Research experience in model eukaryotic
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in conducting experimental, computational, and comparative work to understand how changes in gene expression underlie insect diet specialization and sequestration. They will be expected to come to
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affiliates reflect strengths in machine learning, biological modeling, data ethics, data sovereignty, computer science, environmental data science, climate change policy and modeling, evolutionary genetics
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Apply now Job no:503761 Work type:Exempt Staff Full-time Location:Bethlehem Categories:Postdoc The Schultz Lab is seeking highly motivated PhD-level scientists to lead computational or hybrid wet
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Postdoctoral Association and Yale Office for Career Strategy. Qualifications Candidates should have or be close to obtaining a PhD in either genetics/genomics, evolutionary biology, computational biology, or a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
background in microbial genomics, evolutionary biology, bioinformatics, or computational biology, with an interest in translational applications that connect bacterial evolution to clinical impact. Experience
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: Using biogeochemical evolutionary models to simulate lifeless and inhabited worlds, and Developing disequilibrium-, redox-, and information-based metrics to understand and quantify the influence of life