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Adding a New Layer to Biophysics

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

In recent years, there has been an explosion in applying liquid crystal theory to biological systems. Dense suspensions of swimming microbes, cytoskeletal components, fibrous tissues and bacterial colonies all exhibit properties associated with nematic liquid crystals. Nematic liquid crystals are “liquid” because they flow like a fluid but are “crystalline” because the rod-shaped (or “nematic”) molecules of which they’re made orient in a well-defined direction. But there is a whole zoo of liquid crystalline phases that can be far more complex than simply nematically oriented. In this project, we will seek out and computationally model such biophysical systems.

We are seeking a computational physics/applied mathematics student who is interested in simulating intrinsically out of equilibrium biological systems in which liquid crystal layering is an essential consideration. Such liquid crystals are called smectic liquid crystals. The interdisciplinary basis of this project is essential – we are looking for creative candidates who are interested in exploring biological systems through the lens of liquid crystal theory and computational simulations. The PhD student will develop and use novel numerical algorithms to study biofluids composed of spontaneously flowing filaments that arrange themselves into locally lamellar layers. You will be working in a collaborative and interdisciplinary environment, will become familiar with cutting-edge modelling techniques, and will develop unique simulations. Applicants should have experience with or show enthusiasm to learn numerical modelling. We are particularly eager to see diverse applicants who demonstrate interdisciplinarity, and an eagerness to computationally model exciting and dynamic systems.


Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Physics, Applied Mathematics or a related subject.
A relevant Master’s degree and/or experience in one or more of the following will be an advantage: Statistical physics, biosciences, biotechnology, hydrodynamics, non-equilibrium systems, molecular dynamics simulations, programming with C and python.

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