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Field
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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research assistant support, an annual allocation of funds to support travel and research, a shared computer cluster for parallel computation, a grants office, and several internal research funding
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include high-performance computing, middleware and computing systems. Recent relevant research experience to include: Undertaking research in the area of high-performance / distributed / parallel computing
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of custom computer code, and the processing of large-scale simulations using massively parallel resources such as NERSC and Raj cluster at Marquette. Conduct research with a significant degree of independence
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times through higher parallelization and enable targeted stimulation of hardware faults by adjusting the models. To this end, a simulation environment based on a virtual prototype will be developed using
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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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targeting large-scale computational resources that involves numerical methods, parallel algorithms, inter-process communication and synchronization, and at least one traditional high performance computing
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project management skills, and the ability to prioritise work, work to deadlines and undertake multiple parallel projects Demonstrated ability to identify, assess, test and evaluate technical applications
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international team studying human aging and pathology using computational and molecular methods. Our group develops computational methods for the analysis of large-scale biological datasets, including spatial
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and deployment of HPC systems, ensuring they meet the computational needs and security requirements of a classified environment. Create and maintain detailed documentation of HPC architectures