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, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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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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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
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: Onboarding and Integration, and Innovation and Creative Capacity. The work will focus on investigating how hybrid work arrangements influence learning, collaboration, innovation, and organizational performance
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to learn the required techniques. The following are considered advantageous: Experience with different model plants, including Arabidopsis thaliana. Familiarity with cell wall integrity signalling pathways