Queen Mary University of London

Two PhD Studentships in Medical Decision Support using Bayesian Networks

Queen Mary University of London - School of Electronic Engineering and Computer Science (EECS)

How would you like to play a key role in a major new collaborative project that has the potential to improve the well-being of millions of people? The project, called PAMBAYESIAN (Patient Managed Decision-Support using Bayesian Networks) aims to develop a new generation of intelligent medical decision support systems, applicable to home-based and wearable real-time monitoring systems for chronic conditions including rheumatoid arthritis, diabetes in pregnancy, and atrial fibrillation. The project team includes world-leading researchers from both the School of Electronic Engineering and Computer Science (EECS) and clinical academics from the Barts and the London School of Medicine and Dentistry (SMD). The collaboration is underpinned by extensive research in EECS and SMD, with access to digital health firms that have experience developing patient engagement tools for clinical development. The collaboration with such organisations ensures that there will be excellent future career opportunities for those candidates who ultimately wish to work in the private, rather than public, sector.

The PhD students will focus on developing a Bayesian network (BN) decision-support model for one of the chronic medical conditions, taking account of relevant data and expert judgment. The work is very inter-disciplinary and successful candidates will work with a clinician with expertise in the relevant medical areas, as part of a large team considering the challenges of embedding BN medical decision support solutions into small devices. Further details about the project are at www.eecs.qmul.ac.uk/~norman/projects/PAMBAYESIAN.

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 supervisors are: Prof. Norman Fenton (http://www.eecs.qmul.ac.uk/~norman/) and Dr William Marsh (http://www.eecs.qmul.ac.uk/~william/) In addition to the bursary opportunity, the research group would also welcome applications for self-funded students and encourages applicants to contact relevant potential supervisors to discuss their research proposals.

All nationalities are eligible to apply for this studentship. All applicants should hold a BSc Class 1 or 2:i degree in a relevant discipline (maths, computer science, statistics, engineering) with advanced knowledge in areas such as decision support systems, Bayesian reasoning and probability theory, or knowledge elicitation. Programming skills are also desirable, but not essential. We will provide necessary training and continual professional development. 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 email Prof Norman Fenton (n.fenton@qmul.ac.uk) and Dr William Marsh (d.w.r.marsh@qmul.ac.uk) with subject “PamBayesian PhD: ”, including: a) your full CV; b) transcript of results; c) a cover letter (i.e. motivation statement) of 1 page maximum.

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: http://www.eecs.qmul.ac.uk/phd/apply.php.

The closing date for the applications is 24/03/17.

Interviews are expected to take place in April 5-6 2017.

Starting date: before October 2017 (dates can be flexible).

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