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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and
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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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a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in materials science, nuclear, or mechanical engineering, or a related
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treat one another, work together, and measure success. Basic Qualifications: PhD in Nuclear Engineering, Mechanical Engineering, Thermal-Mechanics, Fluids, or a closely related field. Experience in
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-delivery environment. A PhD is strongly preferred. Candidates must have demonstrated leadership experience in line management and organizational operations, including supervision of technical leaders, budget
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fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, computer science, or a related field completed within
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Qualifications: A PhD degree in accelerator science, or a related discipline (an MS degree in physics or a related discipline will be considered with at least 3 years of relevant experience). Two or more years of
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interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in Earth science, Ecology, Geography, Environmental Sciences, Geosciences, or a related field
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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