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Research Assistant Position: Development of Rail Roughness Measuring Technique and Big Data Analysis
on machine tools and in the field of production engineering. Recent topics of research focus on novel technologies such as additive manufacturing, Artificial Intelligence (AI), Machine Learning (ML), Big Data
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mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In
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simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal needs. Fluid injection or production induces changes in
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machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns and make biomechanical simulations embedded in the generative mechanism design process. In
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mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In
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experiments workflows in areas such as simulation, classification, or anomaly detection using machine learning and deep learning methods, exploring, in particular the performance of Quantum Computing
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Knowledge of Go, ideally also C, as well as Python and shell scripting Knowledge of software and system testing practices and tools Knowledge of computer networks, routing protocols, network protocols and
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relating to computer vision, machine learning and data infrastructure management. Part (50% effort) of this position would also support our work on DeepLabCut - a popular animal pose estimation system https
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in the field of production engineering. A further research focus is on novel topics such as additive manufacturing, Artificial Intelligence (AI), Machine Learning (ML), Big Data Analysis or Industry
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considered. In short, promising applicants have built their own physiological sensor before (e.g., PPG, ECG) and custom PCB as part of an embedded device. To learn more about our research, check out our