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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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qualifications You will lead the computational and AI-driven aspects of the project. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein
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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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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