PhD Studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty
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 causal, or other, relationships from data? Are you interested in algorithms that discover the Bayesian Network (BN) graph for causal inference, and the Bayesian Decision Network (BDN) graph to maximise utility and minimise risk?
The PhD student will specialise in the theory and application of BNs/BDNs, with a focus on structure learning (i.e. learning graphical models). The project will be adjusted to the skills and interests of the successful candidate. For example, theoretical advancements could be assessed by applying them to an area (or areas) of your interest, preferably from economics, finance (excluding stock market), medicine, or gaming.
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 areas such as statistical/probabilistic machine learning are particularly encouraged to apply. Strong motivation to aim for excellence is essential, as are excellent communication skills.
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 at: www.researchgate.net/publication/325848089_Bayesian_Artificial_Intelligence_for_Decision_Making_under_Uncertainty. Applicants seeking further information or feedback on their suitability are encouraged to contact Dr. Anthony Constantinou at [Email Address Removed] with subject “Bayesian-AI PhD”. Please attach your CV, a transcript of records, and your BSc/MSc dissertation/s.
All nationalities are eligible to apply for this studentship. We offer a 3-years fully funded PhD studentship, with a bursary ~£16.5K/year and a fee waiver (including non-EU students), supported by the School of Electronic Engineering and Computer Science of the Queen Mary University of London, UK (www.eecs.qmul.ac.uk). PhD supervisor: Dr Anthony Constantinou (www.constantinou.info). In addition to the studentship, we also welcome applications from self-funded students with relevant background or experience.
To apply, please follow the on-line process (www.qmul.ac.uk/postgraduate/apply) by selecting ‘Computer Science in the ‘A-Z list of research opportunities’ and following the instructions on the right-hand side of the web page. Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? In addition, we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php
The closing date for the applications is July 27, 2018.
Interviews are expected to take place in August 2018.
Starting date: preferably before October 2018 (date can be flexible).