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Position: Research Associate in Mathematics
Institution: Imperial College London
Location: London, United Kingdom
Duties: The project will develop theory and inference methods for data-driven models of cell populations. A goal is to understand how cellular heterogeneity and history limit cellular behaviour in heterogeneous dynamic environments. The project will involve stochastic modelling of cell growth, division, and gene expression to quantify decision making in bacteria and mammalian cells. The successful candidate will have the opportunity to collaborate with leading experimental groups
Requirements: Hold a PhD (or equivalent level of professional qualification) in mathematics, engineering, physics, computational biology; Experience in stochastic modelling of biological systems. For example, using chemical master equations, agent-based models, or stochastic simulation algorithms; A broad and strong background in stochastic processes, statistical inference, multi-scale models and/or single-cell data analysis; Original research results in stochastic modelling, computational biology, and/or theoretical physics commensurate with career stage
   
Text: Research Associate in Mathematics The project will develop theory and inference methods for data-driven models of cell populations. A goal is to understand how cellular heterogeneity and history limit cellular behaviour in heterogeneous dynamic environments. The project will involve stochastic modelling of cell growth, division, and gene expression to quantify decision making in bacteria and mammalian cells. The successful candidate will have the opportunity to collaborate with leading experimental groups Hold a PhD (or equivalent level of professional qualification) in mathematics, engineering, physics, computational biology; Experience in stochastic modelling of biological systems. For example, using chemical master equations, agent-based models, or stochastic simulation algorithms; A broad and strong background in stochastic processes, statistical inference, multi-scale models and/or single-cell data analysis; Original research results in stochastic modelling, computational biology, and/or theoretical physics commensurate with career stage
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