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understanding: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions
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, knowledge-driven models and AI-based decision support can be integrated to support resilient and energy-aware manufacturing systems. Special emphasis will be placed on multi-objective optimization, learning
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the division of Engineering Logistics, Department of Industrial and Mechanical Sciences. We have approximately 15 employees. Here we teach and conduct research in the field of logistics and supply chain
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more on the University website about being a Lund University employee. https://www.lunduniversity.lu.se/about-university/work-lund-university Are you ready to help shape the future of research? Learn
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HIBEAM/NNBAR at ESS and – most relevant for this project – LDMX at SLAC (https://confluence.slac.stanford.edu/display/MME/Light+Dark+Matter+Experiment ). We exploit synergies across these projects, and our
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be part of a larger interdisciplinary research team consisting of professors, researchers, engineers, and PhD candidates. It will be primarily affiliated with the Section for Genetics, Evolution and
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: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School