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research in ML for Health, including HIPAA-compliant compute infrastructure with high memory GPUs and access to Stanford Healthcare data, which includes EHRs for over 5M patients and 100M clinical notes
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computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI environments and tools. Prior experience
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. Experience in parallel programming (MPI, GPU, etc.). Proficiency in biostatistical methods. Ability to work independently and in group settings. Ability to learn quickly and apply new analytic techniques. Job
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analysis, and GPU/FPGA-based acceleration. Ability to work in a multidisciplinary team, collaborate with industry and international partners, and contribute to the design of next-generation electron imaging
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the models and algorithms on GPUs and mainframe computing platforms. Essential Function Yes Percentage of Time 40 Job Duty Mentoring graduate and undergraduate students. Assist the PI (Qi Wang) to mentor
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Qualifications: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent
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parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research projects
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. This support includes access to a Titan Krios and Tundra TEMs, fast network interconnects, all-flash network storage, high core density CPU servers, and AI-optimized GPUs. The position is for 2-4 years depending
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and