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Designing algorithms to unify multimodal signals (physiological, cognitive, contextual, scheduling, and learning data). Developing pipelines that produce structured insights powering the Agentic
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on methods, concepts, and techniques for successfully using information systems and technologies in business processes, developing new business areas, and designing such systems and technologies
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Responsibilities Conduct research in computational methods for environmental and engineering applications. Develop and analyze numerical algorithms, reduced-order models, and machine-learning-enhanced simulation
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include developing algorithms for ultra-fast force feedback and providing open SDKs and APIs for developers. Your Responsibilities Requirements Analysis: Assist in gathering requirements and translating
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modeling and simulation • Development in Finite element and alternative discretization methods (e.g. Lattice Boltzmann methods) • High-dimensional algorithms and high-performance computing
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and evaluation of innovative data-driven and Machine Learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop novel optimization
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together with an IT-expert for the application of machine learning (ML) algorithms for the automated analysis of the recorded mass spectra. Requirements: - Master's degree in Planetary Sciences, Physics
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, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking and computational problem-solving in the Earth sciences at large
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or artificial intelligence (e.g. explanation of and trust in algorithms). The aim of this professorship is to establish solid connections and collaborations between the Department of Philosophy and the Machine
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to explore and develop AI algorithms, frameworks, and hardware architectures for efficient edge deployment in vehicles, with a focus on neuromorphic computing. You will be part of the scientific TUM HN Team