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Description Job description Game theory is rapidly gaining traction in several engineering applications as the natural framework for multi-agent decision making. Yet, unlike optimization, game theory has
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and neutron beamtimes; magnetometry, conductivity, EPR or solid-state NMR on air-sensitive samples. Co-authorship in studies combining experiments and theory. Qualifications PhD in Chemistry, Materials
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modelling, geospatial data science, generative modelling, mobility data analysis, or transport simulation, especially when combined with knowledge of complex systems, network science, resilience theory, urban
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Theory, Department of Environmental and Energy Sciences. Become a part of the team and contributing to research on current and future mobility service usage and attitudes among car and non-car owners
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foundations to industrial and scientific applications. The project will be conducted at the Machine Learning and Decision Making Lab , which focuses on advancing the theory and practice of machine learning and
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develop the data-driven foresight methods to find out. About us At the Division of Physical Resource Theory, we study how societies can transition towards sustainability. Building on physics and systems
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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of functional programming and formal methods. In addition to theory development, your work will have the potential to scale algebraically-accountable architectures to high-stakes, continuous domains, encompassing
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control of dynamic systems. Our work combines fundamental control theory with applications in areas such as transportation, environmental and biological systems, and electric power systems. The division
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of dynamic systems. Our work combines fundamental control theory with applications in areas such as transportation, environmental and biological systems, and electric power systems. The division also teaches