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systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics and mathematical approaches to signal analysis
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connection with uncertainty or data science. Specific fields of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty
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connection with uncertainty or data science. Specific fields of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty
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integrated circuits within the scope of the project, with a particular focus on stochastic computing and implementing hardware-efficient neural architectures. Their responsibilities will include RTL design
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on facility location, storage, processing capacity, procurement, and inventory management. The project combines: stochastic and robust optimization, uncertainty modelling, large-scale supply chain design, and
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limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control, and their applications
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of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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optimization models for electric vehicle fleet charging planning. Activities: • Apply operations research methods (linear and mixed-integer programming, dynamic optimization, stochastic optimization) • Integrate