48 data-"https:"-"https:"-"https:"-"https:"-"BioData" research jobs at King's College London
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internationally recognised for its research into the causes, mechanisms, and treatment of psychotic and affective disorders. The Department of Psychosis Sharing Data Initiative (DPSDI) is a strategic project aimed
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PDRA will contribute to the development of a database of high-resolution equity indicators and apply cutting-edge GeoAI and spatial data science techniques to model and classify population health risk
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candidate will be based within the TOUR team and will hold an honorary contract with The Royal Marsden, enabling access to data and facilitating close collaboration with clinical and research colleagues
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experience in quantitative data analysis, you will examine the links between mental distress and work, care and welfare. You will take forward some selected pre-existing projects (e.g. on the relationship
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(Violence, Health & Society) Consortium, a UK Prevention Research Partnership–funded initiative focused on reducing violence-related harms through high-quality data analysis and rapid evidence generation
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Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Fluency in English Strong skills in
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to ensure data collection and analysis is conducted within timelines. The successful applicant must be able to work independently and collaboratively within a diverse broader research team. The project will
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approaches for managing confidential spatial health data, including differential privacy and other secure geospatial data protocols. This research will advance our understanding of how major infectious
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are looking for candidates to have the following skills and experience: Essential criteria Experience of performing bioinformatics Experience with analysing data from airway samples from patients with asthma
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of Biomedical Engineering & Imaging Sciences. About The Role The research associate will lead the development of cutting-edge multi-modal MRI foundation models. These models will leverage both imaging data and