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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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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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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 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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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
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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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Do you have an interest in working at the intersection of glass science, computational chemistry, and materials science to develop fundamental understanding within a novel glass family? If yes, we
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with the experimental group of Andras Kis at EPFL. Responsibilities The successful candidate will develop and apply ab initio computational methods rooted in many-body (perturbation) theory to explore
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between vehicles will be enabled through shared control, communication protocols, and mission planning, allowing them to function as a coordinated unit. The multi-vehicle platform will be developed by