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the planning and implementation of data quality assurance plans. · Perform day-to-day oversight in program evaluation and primary data collection activities, including: large survey mailing, face-to-face
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management High-performance computing and large-scale data analysis Innovation, entrepreneurship, and technology transfer Scientific communication and transferable skills Training activities will be organised
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measurement approaches, establish experimental test arrangements, characterise fabricated devices and analyse their performance. The work will involve hands-on laboratory characterisation using high-frequency
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initially be for one year, renewable for additional years following performance review and funding availability. Basic Qualifications Ph.D. in computer science, applied physics, applied math, or a related
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, experience working with large-scale geospatial datasets and high-performance or cloud computing, and demonstrated expertise in advanced machine learning. Experience leading research projects, managing budgets
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Actions (MSCA) ITN aiming to train 15 PhD candidates within the newly funded project: Industrial Decarbonisation Using Innovative Low-Grade Heat Upgrade (HeatMinds). The overall aim of the work programme is
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policymakers. A strong track record of high-quality publications is essential, and prior experience in intervention studies is highly desirable. The appointee will work for the research project “Health Programme
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of turbo-compressors suitable for industrial high temperature heat pumps (IHTHPs). The focus will be on the gas flow path considering non-ideal gas behaviour and multi-stage arrangements. Computational Fluid
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and executing research projects. This professional-level position demands a high degree of expertise and judgement in designing studies, analyzing data, and may involve assistants in publishing findings
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for biomedical research Electronic Health Record (EHR) analytics and clinical data integration Biomedical image analysis and quantitative microscopy High-performance computing (HPC) and scalable AI pipelines