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applications Experience with AI model optimization and edge deployment Familiarity with AI hardware acceleration (GPUs, TPUs) Experience with data engineering and AI pipelines This position is hybrid, with a mix
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: Applications accepted all year round Details In this project you will combine state of the art software development approaches for GPU programming with the study of interactions of high-energy particles with
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
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of this role is to oversee the construction and management of a High-Performance Computing (HPC) cluster comprising 4,500 CPU/GPU cores, BeeGFS storage, and Infiniband interconnects. The ideal candidate will
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communicate effectively with the team to debug the model. The OLG model codebase is technically challenging and computationally demanding. For example, it solves dynamic programming problems with GPU
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especially on GPU infrastructure enhancements and improvements as part of Yale’s comprehensive campus investment in AI . As an experienced subject matter expert, you will help lead the system design
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the Alps supercomputer at the Swiss National Supercomputing Centre (CSCS), which features over 10,000 NVIDIA Grace Hopper GPUs, making it one of the most powerful AI-focused computing resources in
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Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as