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inherent complexities. Your role: The doctoral student will conduct research at the intersection of optimization, game theory, and automatic control for complex systems. Their work will encompass both
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their ability to: independently pursue his or her work collaborate with others, have a professional approach and analyze and work with complex issues. Experience in machine learning, algorithmic theory, or code
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of ecological processes involving animals and plants across a range of spatial and temporal scales understanding of raster data processing including the theory and implementation of relevant algorithms
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sustainability demands of future AI-driven applications. Together, these positions provide unique opportunities to advance both theory and applied technologies in 6G, while contributing to standardization
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We are seeking a motivated PhD student in computational materials physics, particularly AI-driven studies of nanomagnetism. The PhD student will work within an active research group in materials theory
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and numerical methods ranging from effective field theory, topological solitons, statistical mechanics of the phase transitions, topological systems to modern microscopic quantum many-body techniques