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Field
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experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication skills in English. Applicants with experience in
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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exploration, optimization, and search algorithms in extremely complex and enormously large spaces motivated by physics and chemistry (RL, BO, Large-Scale Ansatze, …) AI-driven discovery of hardware for some of
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team. Significant software development experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication
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capabilities needed for truly sustainable operations. Research Question: How can AI-enhanced digital twin technologies with advanced optimisation algorithms transform manufacturing processes to achieve
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language. - While our current digital infrastructure relies on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those
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on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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transparency and trade secret claims of regulated actors? And explore legal arguments in support of algorithmic transparency and data access for public interest research? How does EU law balance transparency and
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel