We are soliciting applications for a doctoral student to focus on issues involving modeling methodology, particularly the application of techniques from control theory to health modeling, and integration of such modeling with data collected by ubiquitous sensors. We are seeking a student with excellent character and integrity, and some interest in cross-disciplinary applications of computational science, but for this position are specifically seeking students with methodological depth and strong analytic skills. A successful applicant will be from engineering, computer science, or mathematics background, will have a strong grounding and comfort in applied mathematics, including differential equations, vector calculus, probability theory and statistics. Past work involving control theory, Bayesian methods (particularly Markov Chain Monte Carlo techniques, and particle filters) is a strong plus. This doctoral student would be expected to enter through the Computer Science or Bioengineering program.
Students who do not yet have Masters degrees may apply for this position, with the expectation being that they will remain on for a subsequent Doctoral degree.
While it is unlikely that we will support more than one or two methodologically-oriented graduate students at a time (whether at a doctoral or Masters level), we will consider applicants for the M.Sc. defgree with interest in health modeling and a strong software development background. Such students are sought for software work on several systems that offer support for health modelers.
I regret that the volume of student requests for positions is too large for me to answer all requests individually. To request consideration for one of these positions, please follow the instructions on my student supervision page.