PHD Research THEME
DATA AND DECISIONS
THEME DESCription
The Data and Decisions research theme undertakes research at the forefront of modern mathematical, statistical and computational problems related to core elements of Data Science.
Research topics covered within our theme are optimization, operational research, statistics & uncertainty quantification, as well as their applications in imaging, environmental and medical statistics, epidemiology, actuarial science and financial mathematics.
phd opportunities
The data and decisions theme provides three PhD programmes
The web pages below provide further information about the three PhD programmes offered by the Data and Decisions research theme.
TRAining opportunities
Every student has access to subject specific learning opportunities, broader mathematical training, as well as a range of generic skills training.
Every student can access any of the mathematical & generic skills training activities offered by The Maxwell Institute Training Programme
You will discuss with your supervisor the most appropriate choice of training activities for you, choosing from a range of opportunities offered by our theme, including
- Scottish Mathematical Sciences Training Centre (SMSTC) courses and high level undergraduate courses in data and decisions.
- NATCOR courses
- Academy for PhD Training in Statistics (APTS)
- We run weekly Seminars in Data and Decisions throughout the academic year, inviting many speakers across a range of topics.
- Reading groups and student led activities
past phd projects
Some examples of past PhD projects offered by supervisors in this theme.
- Optimization and Operational Research: “Solution Methods for Some Variants of the Vehicle Routing Problem”
- Statistics: “Efficient Model Fitting Approaches for Estimating Abundance and Demographic Rates for Marked and Unmarked Populations”
- Uncertainty Quantification and Financial Mathematics: “Hierarchical and Adaptive Methods for Efficient and Accurate Risk Estimation”
- Statistical imaging: “Bayesian computation with Plug & Play priors for inverse problems in imaging sciences”.