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://www.rc.ufl.edu/about/hipergator/ ), and the AI NVIDIA GPU SuperPOD (https://news.ufl.edu/2020/07/nvidia-partnership/ ) supporting UF’s campus-wide AI initiative (https://ai.ufl.edu ). These resources are available
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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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resources of 48 GPUs, including 16 * NVIDIA A30, 16 * NVIDIA A40, and 16 * NVIDIA A100. Detailed information about the CEEC Department can be found at: http://www.unlv.edu/ceec MINIMUM QUALIFICATIONS
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. Experience with graph-based data analysis or anomaly detection methods. Exposure to high-performance or GPU-based computing environments. Demonstrated ability to contribute to publications or technical reports
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, retrieval augmented generation, and in-context learning ● Advanced Statistical Analysis: Proficiency in advanced statistical techniques and probability theory ● GPU Programming: Experience with GPU
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for the Master's degree programs in medicine and in molecular medicine. At the department we are approx. 670 academic employees, 500 PhD students and 160 technical/administrative employees who are cooperating across
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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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. Preferred Qualifications: Strong programming skills in one or more of the following: C++, Python, C#, with experience in modern software engineering practices (version control, CI/CD, modular design
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prototypes. Maintain reproducible code, experiments, and model/ dataset versioning; develop deployment artifacts (Docker, CI/CD scripts) and support cloud/HPC model training. Plan, run and analyze pilot
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educational programs with some of the most vibrant research in the nation. Our unique character - shaped by our mission, outstanding academics, facilities, and community life - promotes extraordinary student