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and in cells, (iii) the development and evaluation of mathematical models that integrate binding kinetics into the prediction of drug activity, (iv) the use of biochemical, cellular, and/or in vivo
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Screen reader users may encounter difficulty with this site. For assistance with applying, please contact [email protected] . If you have questions while submitting an application, please review these frequently asked questions . Current Employees and Students: If you are...
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. Ph.D. degree in one of the following areas: Biology, Pharmacology, Bioinformatics, Computational Biology, Statistics, Biostatistics, Computer Science, Computational Mathematics, or other disciplines with
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physical science and/or engineering research. Experience in simulation & modeling of fluid flow. Experience in statistical analysis and quality analysis. Demonstrated knowledge of advanced mathematics
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. Necessary skills include knowledge of post-processing software (e.g., Matlab, R, IDL) and/or statistical/mathematical programming languages (e.g., R, Matlab). Desirable skills include working knowledge
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molecular biology, quantitative genetics, mathematical modeling, or statistical genomics is required. Only persons currently legally able to work in the US will be considered. Preferences: Preference may be
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mechanical/aerospace/industrial engineering, applied mathematics, chemical engineering, or a related discipline earned no more than 3 years ago is required. Experience in modeling and simulation of three
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Basic Qualifications: A Ph.D. in computer science, applied mathematics, plant biology, or related fields. Applicants cannot have received the most recent degree more than five years prior to the date
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Engineering, Chemistry, Physics, Mathematics, or related field from an accredited institution of higher education, conferred by the start of employment Experience with programming in Python Preferred
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engineering, applied mathematics, physics, or related field with an evidence of understanding nuclear nonproliferation and/or the nuclear fuel cycle. Experience applying machine learning techniques to problems