22 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" Fellowship positions at University of London in United Kingdom
Sort by
Refine Your Search
-
Centre (together with UCL, Kings and the Francis Crick Institute) which is a Centre of Excellence in Biotherapeutics. About Queen Mary At Queen Mary University of London, we believe that a diversity of
-
statistical input within long-standing and high impact research collaborations with the Dementia Research Centre at the Institute of Neurology, UCL. The role also includes teaching of postgraduate students
-
improvements in cancer patient care. BCI is also a partner in the CRUK City of London Major Centre (together with UCL, Kings and the Francis Crick Institute) which is a Centre of Excellence in Biotherapeutics
-
is a research project focusing on transforming urban food systems for planetary and population health. The postholders will conduct data analysis and prepare papers for publication using already
-
of implementation analysis). This will include reviewing data from a feasibility study, identifying supplementary data needs, and developing tools for costing in a full trial. The postholder will also contribute
-
estimations in humanitarian and public health contexts by developing reproducible, multilingual workflows for social media analysis, building data pipelines in R/Python, and creating open-source tools for text
-
endemic countries. We are seeking to appoint a Research Fellow to join a research programme that applies advanced bioinformatic, statistical, and population genomic approaches to large-scale sequencing data
-
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. Please note
-
B microarray patch (MAP) technology for the delivery of timely birth doses of the hepatitis B vaccine to prevent vertical transmission of hepatitis B. The post-holder is expected to use data from
-
. The study integrates clinical, microbiological, and data science approaches to generate evidence and tools for safer, more targeted infection management in hospitalised newborns. Key output involves leading