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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly
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consumption while guaranteeing optimal power production. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop graph-based
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree
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the Advanced Production Engineering (APE) research group that deals with the development, optimization and implementation of advanced production technologies and manufacture processes with emphasis on mechanical
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optimized for resource-constrained IoT edge devices, - And what role optimised computing architectures can play in executing these models efficiently. The project will be conducted in close
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Your job Are you committed to reducing food waste? Are you interested in how coordination, dynamic pricing and redistribution may affect food waste? Do you have a passion for applying optimization
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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, and energy solutions. By integrating electrochemistry with advanced materials and engineering, the unit delivers pioneering solutions with real-world impact. Led by Prof. María Cuartero (ERC Fellow) and
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the Advanced Production Engineering (APE) research group that deals with the development, optimization and implementation of advanced production technologies and manufacture processes with emphasis on mechanical
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and ground), and boasts expertise in controlling and deploying them in practice, as well as in designing coordination strategies for them. Our recent work on ML-based co-optimization demonstrates some