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-to-end GPU timing; document limitations and extrapolation behavior. Implement, test, document, and maintain open-source Python/JAX research software; collaborate with researchers to connect trained models
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, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative fields are encouraged to apply. The Research
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large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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. Applicants are expected to have strong programming skills in Python, hands-on experience with PyTorch, and practical experience with GPU computing. Experience with engineering simulation, computational
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programming skills in Python, MATLAB, C++, or similar scientific computing environments, including high-performance and GPU computing. A strong scientific track record, evidenced by high-quality publications
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GPU-centric communication techniques. Collaboration & impact: We actively encourage you to publish your research in high-profile international venues, providing the opportunity to contribute directly to
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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, TensorFlow) with several years of practice Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning