The London School of Hygiene & Tropical Medicine is a world-leading centre for research and postgraduate education in public and global health. Our mission is to improve health and health equity in the UK and worldwide; working in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice.
The Cancer Survival Group wishes to appoint a statistician to apply machine learning techniques in order to identify a sub-population at risk of developing pancreatic cancer using routine electronic health records (EHRs).
This exciting post, funded by the Pancreatic Cancer Research Fund, involves two trans-disciplinary components. EHR data present a unique opportunity in public health because of their population-based coverage and their richness. However, they pose multiple challenges to address, which are not specific to this study. Possible approaches to analyse such high-dimensional data come from the machine learning framework. Machine learning techniques are promising but, in particularly for this study, interpretability of the results derived from these techniques has to be improved.
Pancreatic cancer has the lowest survival rate of all tumours: only 6.8% of patients survive for five years. It is the only invasive malignancy to have shown no significant improvements in survival during the past 40 years. In 2016 almost 8,500 people in England were diagnosed with pancreatic cancer, making it the 8th most common type of cancer.
It is essential that the post-holder has a postgraduate qualification in statistics, medical statistics or equivalent, and can demonstrate an aptitude for applied research in medical statistics. You must have some experience in machine learning techniques and programming in R or Python. We are looking for someone who can work independently as well as collaboratively as part of a team. Some publications, commensurate with your research experience, are expected and some experience of working with EHR data and/or cancer epidemiology would be an advantage. Further particulars are included in the job description.
The post, available from 1 September 2019, is full time and fixed-term for 12 months. The salary will be on the Academic Pathway salary scale Grade 6 at £39,304 per annum (inclusive of London Weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part time staff. In addition to this there are discretionary “Director’s Days”. Membership of the Pension Scheme is available. The post is based in London.
Applications should be made on-line via our website at http://jobs.lshtm.ac.uk. Applications should also include the names and email contacts of 2 referees who can be contacted immediately if shortlisted. Online applications will be accepted by the automated system until 10pm of the closing date. Any queries regarding the application process may be addressed to email@example.com.
The supporting statement section should set out how your qualifications, experience and training meet each of the selection criteria. Please provide one or more paragraphs addressing each criterion. The supporting statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered. An answer to any of the criteria such as "Please see attached CV" will not be considered acceptable.
Closing Date: 21 Jul 2019