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Field
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GPU acceleration, cloud computing, and distributed architectures, to enable efficient analysis of large-scale video datasets. Collaborate with clinical and academic collaborators, external partners
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, implementing input sanitization, and contributing to AI‑safety research. Utilizing GPU/TPU resources, mixed‑precision training, and distributed training frameworks such as DeepSpeed or ZeRO. Prior work
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research practices Experience training and deploying machine-learning models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface
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Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Desirable: B1 Knowledge of Trusted Research Environments out with or within an HPC environment. Skills Essential: C1
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and/or image registration. Experience using high performance computing clusters/GPU based parallelisation. Experience solving diffusion or related equations by Monte Carlo methods. Knowledge of MRI
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storage and archiving solutions to collaboration and analytics tools. ARC also delivers Baskerville; a leading GPU accelerated National Compute Resource (NCR) and supports researchers using specialist
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home to HiPerGator, one of the most powerful high-performance computers at a US public university (https://www.rc.ufl.edu/about/hipergator/ ), and recently added the new AI NVIDIA GPU SuperPod (https
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developing and maintaining research software systems or web-based tools to support laboratory operations · Familiarity with Linux-based research computing environments and GPU-accelerated workflows Before
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skills. Other desirable criteria include: Image analysis experience, e.g. automated segmentation and/or image registration. Experience using high performance computing clusters/GPU based parallelisation
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University is home to Türkiye’s largest GPU cluster, providing advanced infrastructure for leading-edge AI research. The Department of Computer Science and Engineering at Koç University has world-renowned