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for a closed-loop decision support system that can be verified in rehabilitation in many health conditions. This position is open for a postdoctoral researcher in the field of transparent machine learning
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position in the area of Machine Learning for Engineering Design under the guidance of Prof. Mark Fuge, the Chair of Artificial Intelligence in Engineering Design. The general area of the laboratory covers
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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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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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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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variability described in the data by means of uncertainty aware calibration and Bayesian estimation Include how experts currently operate and acquire feelings for the machine they are driving, also based on a
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construction or related fields Knowledge and experience in data analysis and database management, as well as emerging technologies applied to the construction sector, such as BIM, scanning, machine learning
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(or shortly thereafter). Project background The goal of this project is to leverage advanced machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns and
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, machine learning, modeling, and custom hardware, we test our solutions in various real-world projects with industry. Project background We foster a culture of continuous improvement, transparency, and no BS
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proven by publications in this field Have a good understanding of NMR theory and computer simulations Understand the basics of NMR and EPR hardware Be able to optimize and maintain a home-built DNP system