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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
the Gillings School of Global Public Health. The department’s primary focus is to the development and application of research methods to understand the patterns and causes of health and disease, and the
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or Biochemistry (or be close to completion), together with relevant experience, plus knowledge of polymer and materials chemistry and protein, sugar and lipid biomolecule methods. Specialist knowledge
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methods, advanced statistical techniques, and other areas. Responsibilities of the successful candidate include but are not limited to: Leading experiments focused on tobacco and cannabis product emissions
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(PLD, MBE, or similar methods); • Experience in structural characterization (X-ray diffraction, electron microscopy, synchrotron spectroscopy, etc.); • Interest in the study of correlated electronic
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transition analysis to understand the actors, relationships, interdependencies, barriers and enabling conditions influencing technology deployment and scale-up. Design and undertake mixed-methods research
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/instruction tuning methods. * Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization. * Strong programming capabilities in Python
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(for example, efficient sampling from the Gibbs measure, Markov Chain Monte Carlo methods, etc.). Experience in distributed or parallel computing is a plus, but is not a requirement. The position does not
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dynamics simulations and/or protein modeling • Experience with enhanced sampling methods, multiscale modeling, and/or kinetic modeling is highly desirable • Interest in translational applications, including
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: Education: PhD in Microbiology, Immunology, Biochemistry, Molecular Biology, or related field. This position requires a formal degree in the cited discipline area(s) to ensure that candidates have advanced
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genetics. This position offers hands-on training in state-of-the-art experimental and computational methods, including single-cell genomics, chromatin profiling, quantitative trait locus (QTL) mapping, and