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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
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research. BOLD also has access to national compute resources (3M GPU hours on Isambard for the first 1.5 years) and we are working hard to get to 5000 H100 equivalent compute capacity in total across
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experience with probabilistic or computational modelling. Experience with language model evaluation, cognitive modelling, reinforcement learning, goal-directed behaviour, learning theory or large-scale GPU
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. Experience in hardware–software integration, GPU/FPGA acceleration, or translation of research into prototype systems would be an added advantage. Ability to work independently while contributing effectively
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., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members