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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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student, you will focus on mining large Actinomycete genomic, transcriptomic and metabolomic datasets to investigate their transcriptional regulatory network. You will develop and apply the latest
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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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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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’ 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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large national and international network and cooperate with research partners, public authorities, industry, and NGOs. We have state-of-the-art research facilities and Denmark’s only ocean- and arctic
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and dissemination activities within DTU courses and networks We are looking for a candidate who has: A Master’s degree in bioinformatics, microbiology, food science, data science, or a related field
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, including a large international collaboration, offering excellent opportunities for networking with researchers and fellow PhD students, particularly in Sweden, Norway, and Portugal. Responsibilities and
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non