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$150,000. This opportunity is aimed at theoretical physicists pursuing mathematical modeling of the early universe, including its initial conditions, structure, and dynamical evolution. The award is open to
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science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative
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consists of empirical methods (observational and controlled experiments) and theoretical modeling (mathematical, statistical, and computational). The Fellow will work closely with Dr. Otárola-Castillo
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encouraged to apply) A high degree of statistical and mathematical competency with experience applying these skills to problems in genomics, pharmacology, or other relevant areas Extensive experience in
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candidates with strong backgrounds in statistics or mathematics who wish to develop research profiles in statistical machine learning. As a member of our lab, the postdoctoral scholar will develop pioneering
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colleagues and the Quantitative Translational ADME Sciences group to coordinate experimentation and data collection contributing to development of a mathematical model describing tumor penetration. Additional
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within the group and across Research & Development. Who You Are: PhD in Computational Biology, Computer Science, Applied Mathematics, or similar field Demonstrated record of innovative scientific
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Minimum Qualifications: PhD in Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and
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, Computer Science, Physics, Mathematics, or related disciplines within the last 5 years Proficiency in common scientific programming languages (e.g., Python, IDL, Matlab) Preferences: Excellent written and oral
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received within the past five years. Strongly preferred skills: Experience with mathematical modeling of biological systems Experience with population modeling, disease modeling, or similar ecological