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hybrid classical-quantum computing systems, including familiarity with quantum hardware platforms, quantum-classical interfaces, or co-design of hybrid algorithms and system software stacks. Deep technical
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Experiences building satellite imagery preprocessing workflows Experience using Python or other programming languages to develop AI algorithms in PyTorch computing framework Preferred Qualifications: Experience
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), custom accelerators, and next-generation quantum and analog devices. Your work will contribute to a vision of autonomous system engineering in which AI agents understand algorithmic intent, architectural
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participation in workshops, as well as other program development activities Develop new engineering algorithms and/or write components of new software tools Co-author and contribute to various proposals/grants
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data analytics, and manufacturing process optimization. Develop and apply models, algorithms, or data analysis workflows to support machining process understanding, machine tool characterization, process
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learning algorithms. Expertise in object-oriented programming, scripting languages, and modern software engineering practices for research codes. Demonstrated effective written and oral communication skills
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scientific capability, focusing on the co-evolution of algorithms, models, data, and computing architectures. Establish R&D priorities across: AI foundation models trained on scientific and simulation data
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in advanced machine learning algorithms. This position resides in the Manufacturing Systems Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate
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loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component/device models into open-source software tools for integrated
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signature recognition and classification algorithms. Experience with Computational Imaging and Image Reconstruction techniques. This position requires the ability to obtain and maintain a Sensitive