Sort by
Refine Your Search
-
Listed
-
Field
-
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
-
in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The Research Fellow will conduct statistical analyses
-
research networks such as the Centre of Data and Statistical Science for Health (DASH) (https://www.lshtm.ac.uk/dash ) and the Multi-City Multi-Country Collaborative Research Network (https
-
degree, ideally a doctoral degree, in a relevant topic. The post requires strong quantitative skills with expertise in a common statistical package such as Stata/R and the ability to work with large
-
develop research that can inform policy in this important area. The candidate should have a background in a quantitative subject, in particular epidemiology or medical statistics and be familiar with
-
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
-
collaborative research project. The post-holder will join a team with expertise in statistics, cancer epidemiology and health services research and will report to the PI, Professor Richard Grieve, and Co-Is
-
degree, ideally a PhD, in health economics, medical statistics, data science, epidemiology or a related field. A clear conceptual understanding of causal inference methods such as instrumental variable
-
Assistant Professor) to support primarily two new UKRI projects with additional ad hoc statistical analysis and epidemiological interpretation for projects in the thriving laboratories of Professor Chris
-
impacts in the UK. Candidates should have a postgraduate (ideally post-doctoral) degree in epidemiology, medical statistics, public health or similar, and have knowledge of climate change adaptation and