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on power systems and optimization/machine learning techniques. The position is also open to candidates at the final stage of their PhD who have not defended yet. Ability to conduct high quality
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-edge research in the development of novel devices such as field effect, memristor and oscillator which contributes to enabling novel brain-inspired computing architectures for advanced machine learning
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process modeling (physics-based), ii) data-driving modeling (system identification, machine learning), iii) advanced process control and optimization. Demonstrated knowledge of using commercial process
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other PhD students and post-docs on this topic. The successful candidate should be excited about applying machine learning and automation to chemistry, specifically setting up a workflow based on active
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; (ii) considerable reduction of power consumption and cost; (iii) autonomous network control management exploiting artificial intelligence / machine learning (AI/ML) for ultra-high-capacity multi-domain
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. The postdoc will address the human-in-the-loop aspects for model self-auditing, and prediction understanding through visual analytics. The methods will aim at model understanding by machine-learning experts and