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mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and/or adaptive control) with a strong desire to make an impact on energy/power grids are preferred
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: Scientific Area: Data Science and Engineering; Electrical and Computer Engineering; Computer Science and Engineering; Artificial Intelligence; Computer Vision; Computer Science; Data Science; or Mathematical
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Mathematics, Computational Science, or a closely related field, with demonstrated research experience in seismic imaging, waveform inversion, or inverse problems. Strong background in full waveform inversion
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packaging technologies. The main responsibilities are: - Fabricate Si, SiO2, SiN chips for flip-chip fusion and hybrid bonding experiments. - Optimize and maintain fabrication processes, including chip
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, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects in this area and will work
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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mathematics, including medicine (STEM). Through the AI4X-PDF, we seek to nurture the next generation of research leaders working at the intersection of AI and STEM, driving innovation and strengthening
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for the persistence of non-dispersible barite sediment. A detailed understanding of these interactions will enable the formulation of optimized dispersion strategies, potentially enabling rigless P&A operations in
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mathematics, including medicine (STEM). Through the AI4X-PDF, we seek to nurture the next generation of research leaders working at the intersection of AI and STEM, driving innovation and strengthening
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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed