20 postdoctoral-complex-systems PhD positions at Chalmers University of Technology in Sweden
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We invite applicants to join our team of researchers within the area of energy and environmental systems analysis focusing on the transition towards a sustainable transport sector. We are looking
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Autonomous cyber-physical systems (ACPS) have great potential to improve our ways of life, increasing mobility, cutting costs, and saving lives. Considering the complexity of the environments
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Exciting Opportunity in Sustainable Energy Research: Join Us in Advancing Hydrogen Storage Technology! Hydrogen is a critical energy carrier for future sustainable systems. One issue using hydrogen
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education in the development of complex and software-intense systems and is characterized by extensive international cooperation as well as close collaboration with the local industry. About the research
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the development of complex and software-intense systems and is characterized by extensive international cooperation as well as close collaboration with the local industry. About the research project The research
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Join us for an exciting and excellent PhD journey to create the future of maintenance! Explore the fascinating future of Net Zero industries where complex and highly automated equipment need
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computational tools developed by our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel the anthropogenic and natural processes, and their relative
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for a PhD position that combines research in the field of intelligent mission planning and learning-based optimization with real-world applications, in collaboration with Volvo Group. This is an ideal
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ingredients will be explored, maximizing the acceptance of algae-containing foods on the European market. The division of Food and Nutrition Science (FNS) is one of four research divisions at the Department
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Transformers are central to many of today’s most successful AI models, from language understanding to computer vision. Yet, their success remains largely empirical, with limited theoretical understanding