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approaches. Collecting and integrating non-radiological (clinical) data for comprehensive analysis. Optimizing imaging protocols and scanner workflow. Contributing to ethics applications and study preparation
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Siri Pedersen 9th August 2026 Languages English Norsk Bokmål English English PhD Fellowship in Data-Driven Optimization of Multi-Energy Systems Apply for this job See advertisement Job description
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community. We are now looking for a Doctoral Researcher in AI and Quantum-Inspired Optimization for Sustainable Energy Systems Are you passionate about developing AI-based and quantum-inspired solutions
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are experts in systems science; we develop integrated solutions from care of the elderly to space robotics. We are now looking for a postdoctoral researcher in quantum algorithms and optimization for Life
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of the elderly to space robotics. We are now looking for a postdoctoral researcher in quantum algorithms and optimization for Life Science applications. Are you as excited about quantum technology and its future
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Prospect Intelligence & Pipeline Optimization provides strategic leadership for enterprise-wide prospect intelligence, prospect management, and pipeline optimization in support of the institution’s
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing airline operations (e.g., fleet composition, scheduling, routing) based on new
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Berkeley Lab’s Applied Mathematics and Computational Research Division has an opening for a Computer Systems Engineer 3 - Optimization and AI for Scientific Discovery to develop and apply machine
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
: Model development: + Characterizing the key performance differences between current and future aircraft models + Evaluating how these differences affect fleet assignment and network planning + Optimizing
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. In addition, the MCAV approach can be optimized by leveraging information from all individual cavities, rather than relying solely on their vector sum, thereby enabling the development of a MIMO