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Reference: 0181-26 Senior Research Associate in Machine Learning for Speech Processing Department: Phonetics Laboratory / Linguistics and English Language Contract: Full-time (1.0 FTE), fixed-term [18 months
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temperatures and increased snowfall, both of which are important controls on the depth and density of the firn layer. This has implications for the retention and refreezing of meltwater, and associated processes
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research community. We invite applications from experienced and enthusiastic individuals in the field, who have already attained a PhD in a highly relevant field of study. You must be able to demonstrate a
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health difficulties. This role will involve measuring the clinical and cost-effectiveness of the intervention, as well as carrying out a process evaluation to better understand how and why the intervention
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for further funding to extend the post. Candidates with any background that is relevant to this research will be considered. Preferred candidates will have a PhD or equivalent, with relevant experience in data
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considered. Preferred candidates will have a PhD or equivalent, with relevant experience in understanding social impacts of corporate behaviour (in labour, community and/or supply chain contexts). Experience
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activities. Candidates are expected to have a solid Computer Science background and must hold a completed PhD degree in Computer Science or a related field. Ideally, applicants already have experience in
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Intelligence and machine-learning approaches and emerging digital technologies such as non-contact sensors, smartphones, and computer tablets. This theme could also include research in data analytics and
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a PhD in a related discipline (or equivalent experience). You will work closely with members of the MHRG programme from a wide range of organisations, producing reports, publications and other outputs
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). This 2-year position is available at Lancaster University. You should have, or be close to completing, a PhD in Statistics or a closely related discipline. Throughout, you should have demonstrated