65 phd-in-mathematical-modelling-population Postdoctoral positions at Duke University
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, United States of America [map ] Subject Areas: Computer Programming Systems Modeling Biochemistry Atmospheric Science Appl Deadline: (posted 2025/02/19, listed until 2025/05/01) Position Description: Apply Position
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at Duke University for a candidate with expertise in modeling environmental systems with an emphasis on the provision of ecosystem services. The successful candidate would be funded by a grant from the US
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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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collaborative environment at Duke is ideal for our multi-scale modeling research efforts. An earned PhD and previous experience in computational neurostimulation modeling are required as are excellent
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populations across North Carolina and globally, yielding insights into dietary patterns, disease risk, and socioeconomic determinants of health. We are particularly interested in a candidate who could
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and health, either with a background in population health analyses or laboratory research experience in molecular toxicology assays based on the zebrafish model. PROJECTED START DATE: The position is
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experience in mathematical modelling, computational methods, and programming is a plus. · Track record of publications and scientific presentations. · Ability to effectively communicate and
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Description Post-Doctoral Research Associate in modeling materials effect in Urban Heat Island Job Description: A post-doctoral research position is available at Duke University for a candidate with expertise
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consists of four departments with 130 tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its programs in
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recent Ph.D. in microbiology, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative