Are you interested in the theory of causality? Do you want to improve the algorithms we use to discover cause-and-effect and conditional relationships from data, or apply them to a real-world problem of your interest such as healthcare?
Bayesian inference has found application in a wide range of areas. This project will focus on Bayesian machine learning algorithms that aim to discover the structure of a Bayesian network; i.e., the cause-and-effect or conditional relationships from data. These algorithms combine machine learning with search techniques, probability, and statistics. A project in this area can focus on different learning challenges, such as learning from time-series data, missing data, limited data, big data, high-dimensional data, and different types of noisy data.
You can find papers written by our current PhD students who work in relevant areas here: http://bayesian-ai.eecs.qmul.ac.uk/publications/
We are hiring up to two applicants. All applicants should hold, or close to completing, an MSc degree (or BSc with relevant experience) in an area related to machine learning, data analytics or computational statistics/mathematics. Strong motivation to aim for excellence is essential, as are excellent communication skills.
Suitable applicants seeking further information or feedback are encouraged to contact Dr. Anthony Constantinou at email@example.com . Please attach your CV, transcript of records, your BSc/MSc dissertation/s, any publications, and briefly explain why you are interested in a PhD in this area.
The successful applicants will join the School of Electronic Engineering and Computer Science, which has more than 300 PhD students, and will become a member of the Bayesian Artificial Intelligence research lab, as well as a member of the wider Risk and Information Management research group.