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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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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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. Experience with algorithm design, embedded DSP development, multithreaded programming, GPU development, SDR hardware platforms, FPGA development, and/or Linux-based designed tools is desired. Representative
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(clusters/GPU) Language Skills: Fluent written and verbal communication skills in English are required. Knowledge of either French, German or Luxembourgish will be appreciated but are not a requirement to
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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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of six Campus Resource Core Facilities, over 70 Faculty Research labs, and Administrative support staff. Currently, the largest HPC Cluster on campus, it totals more than 10,000 CPUs, 60,100 GPU Cores, and
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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
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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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, 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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-LINAC), thirty Xray-guided linear accelerators, six HDR units, CT, PET/CT. We can access a high-performance computer cluster with the most advanced GPU resources. We also partner with the New York Proton