54 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" positions at University of Glasgow
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Find out more and book your place Information Studies has a vibrant research culture with a growing number of doctoral and masters students, exploring a range of topics in the fields of information
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Find out more and book your place PhD: 3-4 years full-time; 6-8 years part-time; Thesis of Max 80,000 words Apply now Research projects Research projects Self-funded projects Indicative Data Science
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supporting data collection activities in community-based travel and health related studies using varied data collection tools. Tasks include taking informed consent, distributing, and verifying self-complete
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awards across the University. We are investing in our organisation, and we will invest in you too. Please visit our website https://www.gla.ac.uk/explore/jobs/ for more information. Closing date 8 January
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if tradeable points can be used under the Skilled Worker visa rules. For more information please visit: https://www.gov.uk/skilled-worker-visa. As a valued member of our team, you can expect: 1 A warm welcoming
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to natural rock and sediment) and pilot data suggests slag-based anthropogenic coasts can undergo rapid changes in their physical and chemical properties, eg deposition of slag converting a soft coast to a
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Find out more and book your place Our aim is to advance world-leading research and teaching that promotes health equity, addresses emerging health threats, and harnesses data-driven innovation to improve
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data with complementary datasets already collected, such as grain size and magnetic susceptibility. Through statistical investigation, the student willestablish robust connections between different
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allocation and MAC design Networking and joint design of RAN and backhaul Self Organisation of Cellular Networks Use of Artificial Intelligence and Big Data Analysis for Self Organisation Use cases of 5G
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heat and cold exposure on health outcomes in people with multimorbidity, using a combination of population-based data analysis and controlled experimental approaches. The project will analyse large-scale