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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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: Ability to work with large structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and
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for earth system science C++ programming skills and model simulations on GPUs E3SM, CESM, and WRF model experience Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is
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environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment
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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