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multidisciplinary, international, and friendly atmosphere, encouraging creativity, diversity, empathy, and teamwork. You will have a great opportunity to build strong networks with internationally known researchers
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intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and
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by the POLIMA Center and its extensive network of international collaborators. POLIMA is a Center of Excellence funded by the Danish National Research Foundation (DNRF) for an initial period of 6 years
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months at the Technical University of Munich (Germany), gaining international research experience and expanding your collaborative network. As part of your research, you will: Design responsive-switchable
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, take a 4-6 month external research stay, and grow a network across academia, system operators, and industry. Context and motivation The green energy transition is increasingly digital: it relies
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for brain signal acquisition Implementing an on-chip neuromorphic processor with a spike encoder and spiking neural network Developing a low-power spike-based transmitter. Setting up measurement systems and
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’ willingness and ability to adopt the green energy innovations that are essential to achieving a net-zero society. SMEs’ adoption of clean energy technologies is critical to the green transition. Yet, high costs
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research methods highly and often adopt case study, field work methodology or social network analysis. The three-year PhD programme at CBS gives you the opportunity to conduct research under the supervision
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, international team with flexible work organization and support of individual development. The group is involved in a variety of national and European projects and features a strong network of academic and
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to an international network of researchers. As part of the PhD, you will address the critical challenge of incorporating considerations beyond efficiency (e.g., fairness and equity) in urban transportation planning. A