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at international conferences. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process . An
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learning techniques in conjunction with mathematical programming to accelerate the solution of large-scale combinatorial optimization problems that are used to price and specify the parameters of congestion
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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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application If you recognize yourself in this profile and would like to learn more, please contact: If you are excited by the thought of this position and would like to apply, please do so using this link
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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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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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spoken English Working knowledge of current cryptographic algorithms, public key infrastructure and network protocols. Ability to learn new skills and assume new responsibilities. Ability to work
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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
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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