139 parallel-and-distributed-computing-"Multiple" positions at University of London in United Kingdom
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co
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– a £43.6M national programme co-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing
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– a £43.6M national programme co-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing
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an extensive portfolio of in person, blended and online programme titles delivered from our campuses in London and Dubai and customized blended programmes delivered around the world. Programmes are targeted
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CoSector CoSector – University of London is a digital services provider that operates as part of the University of London. It evolved from the University of London Computer Centre (ULCC), established in 1968
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collaborations with external industry meet our ambitious goals for EDI across the sector. They will contribute to a range of Access Programme activities at the National Lab and Challenge Projects in the CDT
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development of new partnerships, connecting into the CoSTAR Network and wider programme delivery. Lead and facilitate collaborative activities in support of enterprise and innovation goals, impact acceleration
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activities, including updating course webpages, producing performance reports, managing social media content and scheduling, and supporting campaign delivery across multiple channels. You’ll also liaise with
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including malaria, have experience and understanding in using multiple metrics for analysis, experience with design and implementation of study protocols, experience of data analysis in Stata and programming
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education strategy across seven departments and multiple LSHTM-based centres, with a focus on building and strengthening skills in artificial intelligence and machine learning for epidemiological research