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The Role and Department The Directorate of Advanced Research Computing (ARC) provides a broad computational capability to underpin and help define the nature of research and innovation that can be
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languages (e.g. C/Fortran) Shared and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical
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increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms. The selected candidate will play an integral part in the group's
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device efficiency. There will be an opportunity to learn the tools required for parallel computing using graphical processing units. This will be used to maximise the computational throughput to enable
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and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries
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will contribute to areas such as the design and analysis of algorithms (e.g. randomized, quantum, approximation, property testing, online, streaming, sublinear, fine-grained, distributed/parallel) and/or
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/Fortran) Shared and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Machine Learning libraries such as Tensorflow or PyTorch Serial and parallel
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-relevant media are a strong plus. Very good organizational skills are highly desirable. Knowledge of parallel computing and use of GPUs are desirable. Supervision and teaching experience is an advantage
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languages, program synthesis, probabilistic programming, and programming languages for emerging areas such as quantum computing and AI. Systems, including distributed and operating systems, machine learning
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an equal-variance signal detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling