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large-scale spiking neural networks. In close collaboration with our Mod4Comp partners (DFG Forschergruppe FOR 5880), you will develop models of performance and energy to guide the co-design of software
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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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, test, and demonstrate resilience strategies that enhance ISAC system robustness against various types of jamming. You will design, implement, and analyze ISAC resilience strategies using advanced
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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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related to staff position within a Research Infrastructure? No Offer Description Your tasks: Development of methods for designing embedded systems based on large language models (LLMs) Investigation
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics