When analysing data at scale, accurate solutions become computationally cumbersome demanding substantial memory and processing power. In situations where time is critical, e.g. for controlling a process, detecting an anomaly or predicting failure from incoming sensor network data, an approximate answer to the right problem often suffices. To this end, this project will explore how we can analyse such data without resorting to storing an expanding dataset using sketching algorithms based on randomised linear algebra suitable. The intension will be to develop tools for real-time diagnostics using streaming data from avionic or automotive sensors.
- explore ideas, methods and algorithms in statistical inference and randomised linear algebra
- implement algorithms in programming languages (Python, MATLAB)
- present results at international conferences and in scientific publications
- work in a team with other PhD students, postdocs, and staff
- Master’s degree (or equivalent) in computational science or scientific computing or similar degree with a focus on applied mathematics and computing
- knowledge/experience in statistical signal processing is an advantage
- interest/experience in applied probability
- programming skills in Python or MATLAB
- fluent in spoken and written English
Start date: September 2019.
Our group's research interests are in computational modelling of physical and engineering systems and inverse problems for imaging, process monitoring, and non-destructive testing. This area entails mathematical modelling, statistical inference and optimisation algorithms. For more info see http://www.homepages.ed.ac.uk/npolydor/
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).