391 high-performance-computing-postdoc positions at University of Glasgow in United Kingdom
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commercial purposes German political and economic thought Medical Humanities Literature and Culture of the 18th, 19th and 20th centuries Psychoanalytical theory Realism Programme Director for German Dr Sheila
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training programme or within 4 years full-time equivalent of completing their training programme are eligible to apply. Clinical work during the PhD will be limited to one day per week. Doctors and dentists
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. 4. Contribute to the well-organised functioning of the laboratory. Maintain laboratory areas appropriately and identify and seek support to rectify any technical faults/problems with equipment
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research communities, ensuring the highest standards of performance, scalability and security are maintained. A research infrastructure technical expert with deep understanding of high-performance compute
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technical knowledge of high-performance and high-throughput computing and component parts. A6 Knowledge of sensitive (including clinical) data processing requirements including trusted research environments
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different decisions by hospital staff involved in infection prevention and control. 2. To perform research using bioinformatics and computational approaches applied to virus genome data. A main focus will be
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Job Purpose To provide high quality and proactive professional services support to enable the efficient and effective management, delivery and development of the School’s degree programmes
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computing. In the UK's 2014 independent research exercise, we were rated top in Scotland for research impact. PhD: 3-4 years full-time; 6-8 years part-time; Thesis of Max 80,000 words MSc (Research): 1-2
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to strengthen business case development and navigate the commercial, infrastructural, and partnership challenges of a 10-year programme. Lead performance reporting to SMG, ensuring alignment with both Investment
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tissue-level determinants of proarrhythmia in an experimental model of long QT syndrome (LQTS) with translation into novel risk stratification approaches using computational modelling. The successful