35 engineering-computation "https:" "https:" "https:" "https:" "UNIV" research jobs at University of London
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About the Role This Research Assistant position is an exciting opportunity to join the Centre for Advanced Robotics at Queen Mary (ARQ) within the School of Engineering and Materials Science. The
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environments. Demonstrated experience in data engineering and ETL workflows required to prepare large, real-world dataset for machine-learning development is also essential. Please note experience in healthcare
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consists of two academics, two senior physicists, two postdoctoral research associates (PDRAs), one technician, and one engineer. The ITk Group collaborates closely with the rest of the Particle Physics
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will develop and apply computational methods for the analysis of cell-free DNA (cfDNA) sequencing data, supporting a growing research program at the intersection of epigenomics and translational medicine
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. The post is based in the School of Engineering and Materials Science. About You This Postdoctoral Research Associate Role will join the multidisciplinary research group of Prof Martin Knight, focusing
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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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About the role We are seeking an enthusiastic Postdoctoral Researcher to join the School of Engineering and Materials Science at Queen Mary University of London. The successful candidate will
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About the Role The principal duty of the post will be to undertake high-level lab-based work, developing the research program as outlined. The role includes data analysis and achieving a steady
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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