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. Design and set up experiments. Deploy your algorithms on machines. Write well-documented code. Prepare demonstrators. Write scientific papers. Guide Master's and/or PhD students. Limited participation in
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. Design and set up experiments. Deploy your algorithms on machines. Write well-documented code. Prepare demonstrators. Write scientific papers. Guide Master's and/or PhD students. Limited participation in
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-driven control algorithms, biomechanical modelling, system identification, machine learning, control theory. Prior experimental experience on human body dynamics and motion comfort. A strong academit track
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related field. Proficiency with machine learning methods and corresponding software packages is a plus. Experience with ultrasonic welding is a plus. Fluency in English and proven academic writing skills
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. Challenges include the identification of suitable metallurgy, the reproducible fabrication of the interconnects, and modeling of their properties. The results of the project are expected to lead a new machine
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Challenge: Solve computational bottlenecks in the modelling of mechanics of metallic systems. Change: Develop new physics-informed machine learning algorithms and predictive models. Impact: Enable
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a PhD in aerospace engineering, applied mathematics, mechanical engineering or other related fields. Affinity with physics-informed machine learning, computational VVUQ (verification, validation, and
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/ The candidate has (or will soon have) a PhD degree in Robotics, Controls, Machine Learning, or a related field. The candidate must be able to work at the intersection of several research domains, have a proven
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been well understood to this date, primarily due to the missing link between data analytics techniques in machine learning and the underlying physics of dynamical systems. The goal of this project is to
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Explore and develop scalable machine learning methods for aerospace structures' design and manufacturing This postdoctoral research aims at exploring statistical methods that enable the data-driven