16 web-programmer-developer-"https:"-"https:"-"https:"-"UCL"-"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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at the intersection of inflammation biology, therapeutic development, and precision medicine, contributing to projects aimed at elucidating pathogenic mechanisms and identifying druggable targets. The programme
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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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in developing countries through excellence in research, healthcare, and training. Our research programme includes basic scientific investigations, clinical trials, epidemiological studies, intervention
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with experience in metagenomics and diagnostics research. The postholder will be clinically qualified and will contribute to studies focused on developing and conducting studies focused on developing
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. The goals are to develop a greater understanding of basic mechanisms of immunological protection versus pathology, and to apply this knowledge to the development of interventions and the identification
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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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and to integrate these lines into the drug-development pipeline to aid the discovery of novel therapeutics that eliminate “persister” parasites. This will involve undertaking research on Trypanosoma
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Trust-funded programme grant. This will involve in-depth biochemical, molecular, genetic and cell biological analysis of the D-arabinanases, leading to the discovery of mycobacterial interactions with
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward