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Field
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development (e.g., PyQt, Tkinter) CUDA for GPU acceleration Scientific computing libraries such as NumPy and SciPy A keen interest in scientific computing, atmospheric sciences, or advanced instrumentation is
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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performance on neuromorphic hardware. What you will do Develop and train CNN and SNN models using frameworks like Keras, PyTorch, and SNNtorch Implement GPU acceleration using CUDA for efficient training
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clusters, including CPU and GPU architectures; Proficiency with job schedulers (e.g., Slurm); Knowledge of parallel and distributed computing principles; Understanding of data security and compliance
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using GPU/CUDA clusters for AI/ML and/or image processing. Proven ability to work in a dynamic environment and support large data systems. Effective documentation skills, including ability to prepare
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XR facilities including VR/AR headsets, GPU workstations, robotics, 3D immersive wall and 3D motion tracking. For more information about the School please visit our sites at Aeronautical and Automotive
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, microscopy, or image analysis Knowledge of large image formats like TIFF or SVS Understanding of GPU acceleration or multithreading in C# Solid understanding of UI/UX design principles Familiarity with
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, such as aircraft tugs, ground power unit (GPU), lavatory service cart, de-icing cart, forklift, golf cart, UTV, and airport shuttles and vehicles. Renders grounds maintenance, such as mowing, cutting trees
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medicine and drug discovery. The King's AI+ strategic investment recruiting 20 AI focused fellows and adding £2m of next-generation GPU capacity to King's Computational Research Engineering and Technology
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reactions. Development and deployment of graphical user interfaces (web apps, desktop apps) Parallel computing (GPU & CPU). Familiarity with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes