Assistant Professor in Epidemiology, Biostatistics or Bioinformatics
London School of Hygiene & Tropical Medicine
London, United Kingdom
In a major strategic expansion of our work on global chronic diseases we are seeking to appoint a number of Assistant/Associate or Full Professors at the interface of epidemiology with rapidly developing technologies. The recruitment of an entire existing research group is possible.
Working together with existing staff the newly appointed individuals will give new impetus to this area in studying the aetiology, prevention and treatment of non-communicable diseases (NCDs), and their interactions with communicable diseases. We expect the new posts to bring expertise and experience in the application of cutting edge approaches to the analysis and interpretation of clinical, molecular, social, and behavioural data. This initiative covers the range of -omics as well as increasing use of novel digital technologies; and linkage and use of large-scale computerised health records. Expertise in bioinformatics, and the use of cloud-based data capture tools (including point of care devices) suitable for use in low and middle income countries (LMICs) would also be of interest.
The Assistant Professor role will be specifically to conduct and publish research of the highest quality and educate students to a high academic standard, and as a result improve the academic standing and sustainability of the School. There will be the opportunity to analyse data and collaborate on publications from related ongoing studies at LSHTM, including studies in low and middle income countries. It is expected that there will be some overseas travel. Teaching of postgraduate students will also be involved as appropriate, particularly on the MScs in Epidemiology and/or Medical Statistics.
Candidates will have a PhD in a relevant field and strong quantitative skills, along with considerable post-doctoral research experience. The successful candidate will have excellent data processing and analytical skills, including high dimensional data. The ability to work under pressure, both in a team and independently, is essential. Ideally, the candidate will also have experience of teaching at Masters level and research degree supervision.