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algebras, quantum affine algebras, algebraic Lie Theory, number theoretical aspects of representation theory, structure theory of Kac-Moody algebras, geometric/combinatorial representation theory, quiver
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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Munich GmbH we started a joint project to build optimization and simulation models for the operations of Terminal 2 of Munich Airport. The joint target is to derive tactical and strategic evolutions
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objective of the research group ‘Crop Physiology’ is to understand the physiology of plants down to the structure and function of genes and proteins as well as relevant mechanisms, which allow optimizing
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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should enable the corresponding production processes and environments to be automatically initialized, processing areas to be generated and the optimal processing tools and their alignment to be assigned
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and
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(MUCCnet: atmosphere.ei.tum.de ) Optimization of an urban sensor network configuration for greenhouse gas and air pollutant measurements using mathematical and physical assessments Analysis of ground-based
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of the research group ‘Crop Physiology’ is to understand the physiology of plants down to the structure and function of genes and proteins. Thereby, relevant mechanisms are identified, which allow optimizing