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non-von-Neumann computing architectures. The project explores hybrid CMOS–spintronic computing systems, leveraging emerging spin-based devices such as magnetic tunnel junctions (MTJs) to enable ultra
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-disciplinary project called Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing and involves close collaboration with Department of Computer Science, University of Copenhagen
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with Bioneer, DTU Bioengineering, DTU Health Tech, and KU, DTU Compute is aiming to create a shared data framework and platform to pioneering models and methods with applications to stems cells
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analog/digital IC design, spintronics, neuromorphic computing, and energy-efficient system architectures. Access to advanced facilities for design, characterization, and system prototyping. Excellent
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Job description - Developing device-to-architecture level models of emerging nanoscale devices (spintronic, resistive, or hybrid) for in-memory and neuromorphic computing. - Exploring hardware-level
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architectural design, including but not limited to ReACT/CodeAct agents, multi-agent systems, self-evolving agents, scalable agentic memory management and Extensive knowledge of existing bioinformatic algorithms
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-waveguide couplings that surpass the time-bandwidth limit of static cavities [Xue2022]. With these components as building blocks, we envision large-scale recirculating circuit architectures [Heuck2023b