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Field
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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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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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, • 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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including historical linguistics, semantics, pragmatics, computational linguistics, phonology, or sociolinguistics. We would like to see candidates who can communicate with and learn from both linguists and
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an industry partner. Experience with research software, data pipelines, and simulations, machine learning, high-performance computing, CANFAR, or advanced data systems. Evidence of mentoring or supervising
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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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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
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mentorship. Develop, implement, train, and validate machine learning and deep learning models for AI-driven prediction of cell physiology, metabolism, and functional behavior, with input from the research team
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) experience with non-destructive testing (NDT) methods (e.g. ultrasonic, thermography, eddy current); (c) knowledge of automation, control systems, and sensor integration; (d) familiarity with AI/machine