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learning for policy optimisation, all within a huge GPU-based simulation with thousands of robots learning synchronously in parallel. The best solutions will then be 3D-printed, and policies will be fine
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needs, such as assisting the team with evaluating evolutionary algorithms for exploring creative new hand designs, or reinforcement learning for policy optimisation, all within a huge GPU-based simulation
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developments; Significant experience with the development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and Publications in top-tier venues in Machine Learning and/or Signal
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theoretical physics, whose responsibilities relate to distributed systems and the GPU optimization of AI algorithms. We expect the team to grow in size considerably over the next few years, and are looking
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: Applications accepted all year round Details In this project you will combine state of the art software development approaches for GPU programming with the study of interactions of high-energy particles with
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requirements. 7. Managing ESXi (Dell PowerEdge) server resources, including modification of RAM, GPU, CPU, Storage Disks when necessary to ensure uninterrupted service to the users. 8. Monitoring performance
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suite of software, and its deployment on the university HPC & GPU based system. The position is primarily research and enterprise, but there would be a contribution of up to 20% to teaching, including
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be expected to utilise or support the development and enhancement of our fire modelling suite of software, and its deployment on the university HPC & GPU based system. The position is primarily
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second