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reliability through systematic validation methods. CAVECORE combines cutting-edge research in AI-enabled robotics with novel evaluation frameworks aligned with EU AI Act requirements. More information: https
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and hardware for future neuromorphic systems. You will also gain profound experience in research-software engineering by actively participating in the developer community of the leading simulation code
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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, as a doctoral researcher, will: Explore energy–delay efficient unconventional computing architectures through both simulation and experimental prototyping Perform iterative hardware–algorithm co-design
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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on massively parallel hardware architectures Combination of programmable logic, tensor processors and general-purpose CPUs for real-time adaption and scheduling services (e.g., AMD Versal platform
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assisted ISAC framework available at the TUM ACES Lab. ▪ Conducting real world experiments to analyze performance deviations between theoretical models, software simulations, and hardware behavior. ▪ Working
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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. Responsibilities and tasks This PhD project aims to optimize the design of hybrid electrical–optical computing architectures: Investigate and design optimal computing and communication architectures for hardware
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of LLMs is desirable. Basic understanding of embedded systems. Familiarity with fault detection, system reliability, or troubleshooting techniques. Strong programming skills (C/C++, Python; hardware