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production; to further develop and calibrate algorithms for tagging boosted Higgs boson decays; to explore novel applications of machine learning to particle physics data analysis and detector hardware; and to
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production; to further develop and calibrate algorithms for tagging boosted Higgs boson decays; to explore novel applications of machine learning to particle physics data analysis and detector hardware; and to
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-based and space-based sensors for autonomous deployment and decision support. Research and develop orbit determination methods based on machine learning and physics-informed neural networks with
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of symbolic regression techniques for automated high-throughput workflows. The postion will involve both implementing new workflows and further develop the underlying machine learning code. Finally to validate
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Intelligence and Machine learning for advanced materials development, ML-guided experiments, or autonomous/robotic experimentation. The candidate should have a strong background in both the computational and
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developing digital and virtual reality capabilities and distance learning platforms. Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU
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potential environmental contamination from naturally occurring and legacy human activity (such as mining), (2) the analysis of large datasets using machine learning and related frameworks, (3) systematic
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flux dynamics to the molecular microbial ecology of methane cycling organisms. The candidate will use a computational modeling framework (including exploration of machine learning approaches
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to measure concentrations of water pollutants. Evaluate treatment performance of water treatment unit-processes. Participate in the developing of research proposals to acquire grants for the development
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on topics including molecular clouds and star formation. Candidates with experience in machine-learning and magnetic field observation/simulation are particularly welcome to apply. Experiences with (sub-)mm