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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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Familiarity with computational drug discovery or molecular modeling Experience with MLOps, reproducibility pipelines, or scalable AI systems As a formal qualification, you must hold a PhD degree (or equivalent
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Enthusiasm and excellent collaboration skills Passion for computational modelling Strong oral and written skills in English As a formal qualification, you must hold a PhD degree (or equivalent) in machine
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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
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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