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position offers the opportunity to develop strong competencies in multi-agent systems, learning and optimization, while addressing a timely and high-impact challenge: the growing use of AI agents in
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are eager to contribute to this rapidly developing field. Our research spans multiple disciplines, and we welcome applicants with backgrounds or interests in areas such as quantum optics, nanofabrication
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they lead to electrochemical failures under low and high voltage bias. The project will also include the development of secondary electrochemical models as digital twins. This PhD project is part of CreCon
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. Experience working with deep learning software stacks, extensive software development experience, and knowledge of machine learning frameworks (such as transformers, torch, Megatron, triton etc.) are pluses
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. You will be part of a team pursuing the development of novel integrated data analysis diagnostic tools that address key challenges in the field of energetic particle physics in fusion plasmas, such as
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is to develop RL methods that can search large policy spaces and support decision-makers in exploring robust strategies under deep uncertainty. Policy problems typically involve many control levers
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development, biomass availability, and sector coupling is experiencing significant growth. The group combines techno-economic modelling, system optimization, and infrastructure planning to address real-world
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development of computational tools that will enable automated structure-property correlation. The research will be supervised by Associate Professor Stavros Gaitanaros. Responsibilities and qualifications We
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Denmark. The work consists of quantitative research, including developing research questions, conducting theory-driven statistical analyses of longitudinal register data, and, where relevant, linking
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effective demand and uncertainty. The research group develops Stock-Flow Consistent (SFC) modeling to examine financial-real sector linkages, while advocating for methodological pluralism in economic problem