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Field
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the
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. In addition, you will deploy and manage project-specific servers, cloud virtual machines, and AI-related infrastructure to support the growing demand for advanced computing in research and education
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Learning, or related fields, completed by the start date. Experience A strong foundation in core AI methodologies—machine learning, deep learning, NLP—coupled with hands-on expertise in Python, PyTorch, and
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one or more of the following areas: Programming, Algorithms and computational complexity, Programming languages and compilers, Computer graphics, and Parallel and GPU computing. A typical teaching
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related functions as assigned. Required Qualifications Bachelor's degree in engineering, computer or information science, or other applied sciences. Three years of experience in digital signal processing
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, and maintaining machine learning models for inference, optimizing model and hardware performance, troubleshooting AI/ML solutions, and integrating them within the broader application environment
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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reasoning tasks such as camera pose estimation, view synthesis, and 3D scene understanding. Daily activities include coding and debugging deep learning models, conducting experiments on GPU servers, analyzing
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence