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experimental PhDs and Canon Production Printing to enable data-driven ink optimization. Why Join? Work in the multidisciplinary Processing and Performance of Materials group Work at the forefront of sustainable
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to provide scalable, faster-than-real-time capable methods and tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems. Your Job This postdoctoral position
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of energy materials Develop and optimize advanced X-ray optical concepts with emphasis on maximizing photon flux, transmission, stability, beam quality, and experimental flexibility Design next-generation
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for studying nanoscale magnetic and magnet-superconductor hybrid systems, and implement these in numerical simulation codes. Interested candidates should send a motivation letter, a CV and the contact
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optimization models. Investigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification
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network optimization for 6G/FutureG wireless networks especially on the Internet of Intelligent Things under the supervision of Dr. Lingjia Liu (https://lingjialiu.ece.vt.edu). Successful candidates will
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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of mathematical modeling, with a particular focus on stochastic modeling, optimization and more recently machine learning. Indeed, over the last years, the team’s activity has been marked by a strong shift toward
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precision-aligning free-space optical bench components. Theoretical Foundations: Robust prior experience with the numerical modeling and design of optical or electromagnetic wave structures. Departmental
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convective hazards. The model development will be aided by synthesizing observational data and high-resolution numerical simulations to gain new conceptual insights on cloud life cycles. Potential