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parallel and GPU computing is most desired.PX4, Pixhawk or equivalent. Ability to work well with team members and good communication skills are essential. To apply, please send your cover letter, CV and
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, and guidelines to ensure continued relevance and effectiveness. Evaluate incoming resource and procurement requests (e.g. GPU workstations, servers, and cloud credits) in compliance with university
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implement ensemble learning algorithms and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency
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lead in designing, deploying, enhancing, and managing our HPC infrastructure. This infrastructure includes GPU clusters (B200/H200/A40), liquid-cooled CPU cluster, and a cloud-based HPC system, with
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electromagnetics A good understanding of the physics of scattering and antenna radiation Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired More Information