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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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in machine learning and deep learning methods. Knowledge in OpenCV, ROS, and Gazebo. Well organized and excellent time management skills. Excellent command of English and communication skills
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in machine learning and deep learning methods. Knowledge in OpenCV, ROS, and Gazebo. Well organized and excellent time management skills. Excellent command of English and communication skills
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postdoc to analyze or synthesize tactile information (e.g., time series data) using supervised and unsupervised learning models. Besides, you will develop your a) writing and communication skills by
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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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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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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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on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research
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We live in an era where artificial intelligence (AI) stands as a beacon of innovation, where advances in machine learning (ML) profoundly impact many aspects of our society. Nevertheless, the use