-
environment. Further information on the programme is available at: PROGRAMME | CREATE PhD Programme (create-phd.org) This programme is aimed at supporting health professionals who wish to undertake rigorous
-
, development, implementation, and coordination of research and laboratory protocols to investigate transmission of enteric pathogens in low-income households of Salvador, Brazil within the context of large-scale
-
analysis. Experience analysing large data sets is also essential. Further particulars are included in the job description. The post is full-time 35 hours per week, 1.0 FTE and fixed-term until 29 February
-
data integration for epidemiological innovation, funded by the Wellcome Trust. The CONNECT project offers the opportunity to work with the largest population-based UK cohorts, such as UK Biobank, CPRD
-
large health datasets on topics including pharmacoepidemiology and non-communicable diseases. The post requires strong data management and quantitative skills with expertise in a common statistical
-
for cardiovascular disease in this patient group using linked electronic health record data. The post offers an excellent opportunity to develop expertise in risk prediction methodology for electronic health records
-
exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
-
to important public health topics. Studies will include descriptive epidemiology and use emulated target trial approaches for robust causal inference within large national health datasets. The post offers
-
of a large multidisciplinary collaboration between LSHTM and Oxford Brookes University (lead partner), UCL, LSE, University of Leeds, University of Edinburgh, and a wide network of UK stakeholders
-
environmental epidemiology research team at LSHTM to work on a new UKRI-funded study in the field of climate change and health entitled THERM-UK. This is an exciting opportunity to be part of a large