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ensemble structures. As an Empire AI-funded fellow, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This
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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and
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or TensorFlow. Advanced programming and high-performance computing skills, including proficiency in Python and/or C/C++, experience with GPU acceleration, and the ability to develop, test, and maintain research
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computing environment that includes GPU clusters, large-memory servers, and an NVIDIA DGX B200 system. These resources support the training of large multimodal models involving audio, video, language
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background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems
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Fellow of AAPM. Details about Dr. Ren’s profile can be found at the following link: https://www.medschool.umaryland.edu/profiles/Ren-Lei/ Equipment includes a computer cluster with high-end GPUs, and state
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage