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that algorithmic parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but
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. Because of the specific structure of the inference problems occurring in metabolic models, direct application of these MCMC algorithms is, however, not possible. In this project, you will bring MCMC methods
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for autonomous drone systems Multi-agent cooperation using Reinforcement Learning and control barrier functions Mission planning for aerospace systems using evolutionary algorithms and Reinforcement Learning
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data analysis and develop sophisticated mathematical models for simulating power system behaviors under various scenarios. Development and Testing: Design and develop control algorithms to enhance grid
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, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
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their reliability and resource efficiency during production and operation. The »KI-unterstütze Simulation« team combines physically based simulation approaches with efficient and advanced mathematical algorithms and
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, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data
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. Therefore, we employ a broad range of artificial intelligence algorithms that enrich process data with sophisticated domain knowledge and semi-supervised approaches to incorporate unlabeled data. We