Deadline
Varies
Value of Scholarship
Renewable
No
Applications are invited for a funded PhD Studentship within the Risk and Information Management (RIM) Research Group in the field of medical decision support, to work on decision-making and prediction systems.Â
A successful collaboration with the Trauma Science group, in the Blizard Institute of the School of Medicine and Dentistry, has developed new tools using Bayesian networks to support clinical reasoning: one example greatly exceeds the performance of the existing âscoreâ, requiring only the data available when a patient first arrives in hospital. The network models represent causal influences and are built using multiple sources of knowledge and data, supported by evidence from literature and experts.
The overall research goal is to develop and deploy probabilistic models of physiology and injury with a wider scope that can support multiple decisions, sufficiently well evidenced to be widely accepted, with automatic detection of relevant additions to the medical literature that may require the model to be revised. Improved decision support requires greater use of the explanatory capability of the models and improved interfaces; more automated model construction is also needed to reduce the costs of developing models. Depending on the candidateâs previous experience, it is likely that the initial task will be to develop and validate a new model for a clinical decision problem. He or she will then select specific research objectives contributing to the overall research goals.
The candidate will be supervised by Dr William Marsh, in the School of Electronic Engineering and Computer Science and will join a group of 12 PhD students and 3 post-doctoral researchers in the RIM group working on related aspects of decision support. The candidate will also be expected to collaborate closely with a researcher supervised by Mr Nigel Tai FRCS, a Consultant Vascular Surgeon at the Royal London Hospital, Whitechapel, and a Senior Lecturer in the Queen Mary School of Medicine and Dentistry. This collaboration provides an optimal setting for access to clinical data and knowledge as well as a context to validate the results of the research.
Candidates should have a first class honours degree or equivalent, or a strong MSc degree, in computer science, mathematics, physics or engineering. A good knowledge of probability and a strong interest in decision support are essential. Previous experience with Bayesian networks and competence in programming are desirable but not essential.
The studentships will be based in the School of Electronic Engineering and Computer Science (EECS) www.eecs.qmul.ac.uk at Queen Mary University of London. Two sources of funding are available:
- An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.
- An International studentship is available to candidates without UK residency and is for 3 years. This studentship covers student fees and a tax-free stipend of £15,720 per annum.
Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting âComputer Scienceâ in the âA-Z list of research opportunitiesâ and following the instructions on the right hand side of the web page.Â
Please note that instead of the âResearch Proposalâ we request a âStatement of Research Interestsâ, which should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area, including probabilistic model and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. 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 31 January 2014. Interviews are expected to take place during February 2014. Please contact William Marsh on d.w.r.marsh@qmul.ac.uk for any queries.
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