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Field
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Internet is undergoing a fundamental transformation from cloud architectures to a distributed continuum spanning edge, cloud, and space. This shift demands revolutionary approaches to how we orchestrate
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new paradigm in the validation of autonomous driving software, focusing on the intersection of Large Language Models (LLMs) and Autonomy 2.0 / End-to-End (E2E) approaches. While E2E models promise
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the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators
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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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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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effort supported by the Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg AI, Autonomous Systems and Software Program (WASP), a WASP-WISE NEST. This interdisciplinary setting
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description Conduct a literature review on SSMs, LLM architectures, and hardware acceleration techniques. Investigating design choices that optimize the model across both the hardware and software stack. Design
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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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than conventional structures. Superstructure optimization, which uses a predefined architecture with numerous unit operations and possible paths, offers an alternative for identifying non-intuitive