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Application Deadline 26 Aug 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The DYNATERRE project contributes to the transition toward a
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-reviewed publications in high-quality journals. • Proficiency in programming and modelling tools (e.g., R, Python, C/C++, Julia). • Excellent quantitative and analytical skills, with experience handling
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interventions in type 1 diabetes complicated by impaired awareness of hypoglycaemia – including the My Hypo Compass programme developed within Newcastle University. You will play a critical role as a named
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storage, research data management and training services Providing or arranging training for researchers in specialized areas of Advanced Research Computing, e.g. Big Data processing, parallel programming
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tools such as Bash, Python, or Ansible. Experience with performance tuning and benchmarking tools for HPC environments (e.g., Ganglia, Grafana, or similar). Experience with parallel programming frameworks
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lead several parallel implementation efforts for Merit's business systems, organization risk preparedness, and corporate software development needs. Manage medium to large-scale projects determined by
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responsibilities The Programme Manager will have a key focus on supporting Programme wide data strategy and access for the Cancer Data Driven Detection (CD3) Programme, in addition to wider delivery support for
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of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility