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the lifecycle of industrial systems. As machine learning sees broader adoption, companies are increasingly required to ensure the safety of machine-learning-enabled systems. The reliance on training data and the
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how a novel machine learning-based methodology leveraging reinforcement learning with human feedback and multi-objective optimisation can be realized to generate new and even improve existing work plans
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well connected to the machine and transportation, high precisionindustriesand I am eager to learn how academic research can be linked to industrial innovation roadmaps. During my PhD I want to grow
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citation record must be focused on AI; or alternatively (B), machine learning engineers with an AI-focused PhD and demonstrated 2-year industry experience in AI development Applicants must have in-depth
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of data analysis, time series analysis, machine learning and algorithm development. have knowledge on machine learning with Python or MATLAB. are very fluent in English, both spoken and written. possess
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occupational asthma, dust lung, auto-immune diseases, cancer, etc). · Strengthens existing research lines and brings complementary and/or additionally new expertise by working closely with the members
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wireless communication, signal processing, digital, analog and mm-wave design, and machine learning. This is a unique opportunity to develop innovative, multi-disciplinary technology and shape future
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initiated research Advantages strengthening the candidate’s profile, but not explicitly required: Knowledge of machine learning and system optimisation; Python or MATLAB programming. Having published as (co
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power consumption trends or including the energy penalty of machine learning solutions themselves. And the energy efficiency at the transceiver hardware will be put in a broader perspective of
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resistance, via machine learning approaches. This doctoral project also foresees three secondments, each for the duration of three months, during which you will have the opportunity to visit partner