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Field
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster analysis, to characterize populations with diabetes and
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Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and
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machines Machine Culture Ongoing work on social reinforcement learning and evolutionary optimization of social strategies Our aim is to advance the scientific knowledge of human-AI systems by understanding
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, Physics, Computer Science, or a related discipline. Demonstrated research experience in computational chemistry, atomistic simulations, machine learning, or artificial intelligence for materials and
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, • Vehicle dynamics and control, • Machine learning and AI, and • Data privacy and security. In addition to research, the selected candidate is expected to help draft research proposals and supervise
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demonstrate how innovative methodologies can generate new knowledge within art history. Relevant topics may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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biophysics and machine learning for the accurate prediction and design of functional enzymes; and explore their real-world applications. Job Requirements Ph.D. in a relevant area, e.g., Chemical Engineering
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science, machine learning, AI, or any computational science discipline of interest to the Computing Sciences Area and Berkeley Lab. Fellows apply advances in these fields to computational modeling