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(28nm or below) Hardware-aware modeling of state-space inference pipelines Simulation and synthesis of the architecture using EDA tools Benchmarking against transformer-based hardware accelerators Work
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applications Co-integration of SSM-SNN cores with RISC-V processors in CMOS Hybrid analog/digital interfaces for spike generation and propagation Event-based communication protocols for ultra-low-power operation
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of research results in scientific journals and conferences. Qualifications: Good understanding of solid mechanics and preferably modeling of damage and/or fracture. Experience with experimental work and data
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. An external research stay of approximately 3 months in a collaborating institution is a mandatory part of this PhD fellowship position. You will work in close collaboration with experienced researchers and
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of code to utilize GPU-acceleration on DTU’s high-performance computing cluster or other HPC systems. You will also analyze realistic physical implementations of the architectures you explore, with a
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research with possible continuation as a research assistant or in a possible PhD degree programme Place of employment and place of work The place of work is the Department of Animal and Veterinary Sciences
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. The approach will be validated by application to industrial production at a partner company. The position includes both theoretical and experimental work and carries project as well as academic responsibilities
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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. The research should focus on low-power embedded systems, multimodal sensing (including wearable shoe-based platforms), and edge-cloud computing with serverless and federated learning techniques. You will work
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Job Description Are you driven by the idea of creating a more sustainable future through groundbreaking research? Do you want to work in a collaborative environment where your contributions can