Our research group is highly interdisciplinary, involving both an experimental section where researchers with a background in molecular biology and biophysics are experimentally studying genome evolution and gene regulation at the single cell level in bacteria, and a theoretical section where researchers with backgrounds in theoretical physics, computer science, and applied mathematics are using techniques from Bayesian probability, evolutionary theory, dynamical systems theory, and stochastic processes, to study the function and evolution of genome-wide regulatory networks in cells

Requirements:

For this position we are looking for candidates with strong mathematical and computational skills that are excited to work in the area of quantitative modeling of either gene regulatory networks at the single cell level or microbial genome evolution. Depending on the project, experience in areas such as next-generation sequencing data, stochastic processes, dynamical systems theory, population genetics, and Bayesian statistics will be desirable

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PhD position in theoretical and computational modeling of gene regulatory networks and genome evolution Our research group is highly interdisciplinary, involving both an experimental section where researchers with a background in molecular biology and biophysics are experimentally studying genome evolution and gene regulation at the single cell level in bacteria, and a theoretical section where researchers with backgrounds in theoretical physics, computer science, and applied mathematics are using techniques from Bayesian probability, evolutionary theory, dynamical systems theory, and stochastic processes, to study the function and evolution of genome-wide regulatory networks in cells For this position we are looking for candidates with strong mathematical and computational skills that are excited to work in the area of quantitative modeling of either gene regulatory networks at the single cell level or microbial genome evolution. Depending on the project, experience in areas such as next-generation sequencing data, stochastic processes, dynamical systems theory, population genetics, and Bayesian statistics will be desirable

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