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phlebotomy. Additionally, the GPU is home to the GI Division's Motility program offering short and long motility studies, Bravo, Impedance Probes and EndoFlip diagnostic tests. The GPU is also home to
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Sheffield to enable massively parallel processing of ABMs on NVIDIA graphics processing units (GPUs), without the need for specialist understanding of GPU programming or optimisation. This project will
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strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems.Reconfigurable and Spatial hardware, such as
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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high-performance GPU cards for enhanced processing capabilities. For more details, please refer to: https://robinson.gsu.edu/academic-departments/insight/innovation-labs/insight-lab/ Disclaimer: This job
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(UTC) Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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are essential, particularly for developing optimisation and reconstruction algorithms. Knowledge of GPU programming (CUDA, OpenCL) is a plus; Experience in data analysis using machine learning is a strong asset
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Summary/ Department Summary: The GPU is a procedural unit running 5 days a week for both ambulatory and inpatients. The GPU performs a wide range of non-sterile procedures: examples include; therapeutic
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), with collaboration from PhD students and external partners. The researcher will benefit from an active local community in AI and access to GPU computing infrastructure. Where to apply Website https