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Field
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is of advantage: Knowledge of parallel programming and HPC architectures, including accelerators (e.g., GPUs) Experience in modelling and simulation, ideally in the field of energy systems Experience
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-dimensional biological datasets. Familiarity with GPU computing and high-performance computing (HPC) environments. Other Requirements Ability to work collaboratively with researchers across computational and
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background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems
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optimizing compilers, the classical and quantum fragments are separated in efficient implementations adapted to the changing QPUs and GPUs architectures. The candidate will work at the intersection
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for accelerators, such as GPUs or FPGAs. Experience in refactoring or porting large codebases (over 100k source lines of code). Background in supporting scientific code on HPC systems or familiarity with components
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with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer
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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
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the College of Engineering. UNLV GPU Cluster (named RebelX) is also available for A.I. research and education. Detailed information about the CEEC Department can be found at: http://www.unlv.edu/ceec MINIMUM
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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for mechanical, electrical, cooling, and infrastructure systems that underpin Cornell's computing environment, including High-Performance Computing (HPC) and Graphics Processing Unit (GPU)-intensive workloads