20 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"UCL" Fellowship positions at University of London
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& International Health is seeking to appoint a Research Fellow in Health Data Science (with a focus on machine learning) to NeoShield , a multi-country implementation research programme focused on neonatal sepsis
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with Professor Emma Thomson and Professor Michael Marks, and collaborators at UKHSA. This work is a collaboration between the London School of Hygiene & Tropical Medicine (LSHTM), the Rare and Imported
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Technology. Applicants should hold a doctoral degree in public health, epidemiology, or a related discipline, and have strong experience in longitudinal data analysis and advanced causal inference methods (e.g
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. Supported by a BHF Programme Award, this role offers an excellent opportunity for a motivated clinician to pursue academic cardiology in a leading research environment. The Fellow will contribute
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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
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The Department of Infection Biology brings together pathogen
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must have a postgraduate degree, ideally a doctoral degree, in a relevant topic. The role requires proven expertise in data science or related fields, with strong skills in quantitative analysis applied
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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
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The Department of Infection Biology brings together pathogen
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. 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