School of Electronic Engineering and Computer Science

PhD Studentship in Bayesian Artificial Intelligence

Application closing date: 15/03/2019
Start date: As soon as possible or by October 2019
Research group:  Risk and Information Management  
Duration: 3 years
Funding: Available

 

Do you enjoy working with probabilities, data, and algorithms? Are you interested in the theory of causality? Do you want to improve the methods we use to discover cause-and-effect relationships from data and knowledge? Are you interested in algorithms that discover Bayesian Network (BN) graphs for causal inference, and Bayesian Decision Network (BDN) graphs for optimal decision making; i.e., maximising utility and minimising risk as in game theory?

The PhD student will specialise in the theory and application of Bayesian Networks, with a focus on structure learning; i.e., the automated discovery of the graphical structure/network. The PhD studentship is part of the EPSRC project on Bayesian Artificial Intelligence for Decision Making under Uncertainty. You can read more about the project.

All applicants should hold, or close to completing, an MSc degree (or BSc with relevant experience) in an area related to computer science, statistics, or mathematics. Applicants with advanced knowledge in Bayesian methods, or with experience in publishing conference/journal publications, are particularly encouraged to apply. Strong motivation to aim for excellence is essential, as are excellent communication skills.

Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Anthony Constantinou (www.constantinou.info) at a.constantinou@qmul.ac.uk with subject “Bayesian-AI PhD”. Please attach your CV, a transcript of records, your BSc/MSc dissertation/s, and any conference/journal publications.

All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a tax-free bursary currently £16.8K/year for 2018/19, and a fee waiver (including non-EU students) supported by the School of Electronic Engineering and Computer Science (EECS) of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). The successful applicant will join EECS that has more than 300 PhDs, and will become a member of the Bayesian Artificial Intelligence Research Lab, and a member of the Risk and Information Management (RIM) research group.

To apply, please follow the on-line process at http://www.eecs.qmul.ac.uk/phd/how-to-apply/

Please note that instead of a ‘Research Proposal’, we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: a) Why are you interested in the topic described above?, and b) What relevant experience do you have?

In addition, we would also like you to send a sample of your written work. This might be your BSc or MSc dissertation, or a published conference or journal paper.

The closing date for the applications is March 15, 2019.

Interviews will start before the deadline and continue shortly after the deadline.

Starting date: As soon as possible or by October 2019.