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- 
                Field
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                for scientific computing to support users in the process of solving increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms 
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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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                management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding 
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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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                . You have experience in matrix algorithms, data compression, parallel computing, optimization of advanced applications on parallel and distributed systems. An excellent scientific track record proven 
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                for an accurate simulation of time-dependent flows, enabling sensitive applications such as aeroacoustics. Furthermore, the high scalability on massively parallel computers can lead to advantageous turn-around 
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                ) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel 
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                programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or 
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                the process of solving 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 
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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