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:

Candidates should have PhD in an area relevant for the project and strong mathematical and computational skills Experience in areas in one or more of the following areas is highly desirable: next-generation sequencing data, stochastic processes, dynamical systems theory, population genetics, and Bayesian inference. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental data and experimental collaborators

Text:

Post doc 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 Candidates should have PhD in an area relevant for the project and strong mathematical and computational skills Experience in areas in one or more of the following areas is highly desirable: next-generation sequencing data, stochastic processes, dynamical systems theory, population genetics, and Bayesian inference. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental data and experimental collaborators

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