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Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network. This is partly
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to benefit from latest advances in hardware technologies the ICON model was ported to run on Graphics Processing Units (GPUs) and is one of the first model that can be used in production
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- into a GPU-enabled and parallel code to run efficiently on state-of-the-art exascale hardware Designing implementations and reviewing community contributions of library features and new statistical
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, and Ising machines. The group possesses strong expertise in magnetic, electrical, and optical characterization techniques (particularly micro-focused Brillouin Light Scattering) and leverages GPU
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molecular dynamics simulations and was specially designed for parallelisation on GPUs. It is open source and licensed under the LGPL. Details can be found on the website https://halmd.org Job-Description
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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experience. PACU/Critical Care/GPU experience strongly preferred. Experience working with patients 6 weeks in age to 25 years of age preferred. Licensure/ Certifications: Current Massachusetts license as a
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, advancing bioinformatics-driven research, facilitated by exceptional computational infrastructure including a centrally administered high-performance CPU and GPU cluster and network storage. This includes
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and a new GPU cluster. Opportunities for professional growth and career advancement. Collaborative and inclusive work environment that fosters creativity and innovation. Application of Domain Expertise
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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision