Identifiability analysis for stochastic models in mathematical biology
We seek a PhD student to work in my research group who will be co-supervised by Christopher Drovandi (QUT) and Oliver Maclaren (Auckland). The student will work on a project at the interface of applied mathematics and applied statistics, by focusing on developing and analysing computational techniques to assess parameter identifiability in stochastic models used in mathematical biology. The project is funded through the Australian Research Council.
The project will involve the development and implementation of novel stochastic models of cell biology processes. The modelling will focus on cell migration (diffusion, chemotaxis, cell-sorting) and we will develop stochastic models that reproduce standard experimental protocols. We will use methods from computational statistics for parameter identification and, in particular, parameter identifiability. We will develop new methodologies to assess parameter identifiability in a stochastic setting. The main aspect of the project will be to develop novel methods to assess parameter identifiability. Such methods will be applied to real experimental data sets.
The successful applicant will be based in the School of Mathematical Sciences at QUT, joining other PhD and Master of Philosophy candidates in my research group.
Applications: Applicants should hold a first class Honours degree or equivalent (such as an MPhil degree) in applied mathematics, physics or engineering. Strong academic performance, excellent written communication skills, and expertise in computational techniques (C++, MATLAB, Python and/or Julia) are highly desirable.
The applicant must be eligible to enrol in a PhD at Queensland University of Technology. A scholarship of approximately AU$27,000pa is available. The scholarship is tax exempt for full-time students, and is to support living costs for up to 3 years for doctoral students.