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01.10.2025, Wissenschaftliches Personal This PhD position is part of the interdisciplinary TUM GNI project AUROrA – AI-Driven Urban Flood Resilience: Integrating Earth Observation and Architectural
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), técnicas y herramientas software de análisis de datos, machine/deep learning (Pandas, SHAP, TensorFlow, etc.) y específicas de análisis de imágenes, estadística, simulación, entornos cloud (tipo Kubernetes
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, adapt locally to users and environments, and remain computationally efficient. These models will be explored and benchmarked in software, with selected approaches translated into embedded prototypes
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will explore advanced THz front-end architectures and evaluate their applicability in real-world scenarios. Key objectives include: Exploring use cases and front-end architectures in the 300–500 GHz
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? What are the optimal hardware-software architectures for deploying the above methods, test and validate the model? Applicants should fulfil the following requirements: MSc either in biomedical
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. Map, document and analyse settlement typologies in selected peri-urban sites, using field information, spatial data, and architectural methods and techniques. Investigate the role of policy, governance
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highly motivated Ph.D. candidate. The background is preferably in electrical engineering, computer architecture, control, computational neuroscience, or similar fields. Unconventional backgrounds supported
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solutions, secure ICT architectures, grid planning, system services and scenario-based simulations. We work closely with grid operators, energy suppliers, and other stakeholders in the power system industry
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domains. Develop hybrid AI architectures combining symbolic domain knowledge, real-time data streams, and probabilistic inference. Design and evaluate decision support tools capable of interacting with
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ECUs are reshaping how connected and autonomous vehicles are designed and validated. The focus is shifting toward creating high-fidelity virtual ECUs that support software-defined vehicle architectures