10 machine-learning Postdoctoral research jobs at Delft University of Technology (TU Delft)
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knowledge in software integration methods. Knowledge in machine learning and deep learning methods. Knowledge in OpenCV, ROS, and Gazebo. Well organized and excellent time management skills. Excellent command
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knowledge in software integration methods. Knowledge in machine learning and deep learning methods. Knowledge in OpenCV, ROS, and Gazebo. Well organized and excellent time management skills. Excellent command
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following aspects will help you stand out: Knowledge of data-driven control algorithms, biomechanical modelling, system identification, machine learning, control theory. Prior experimental experience on human
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, focusing 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
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. Affinity with physics-informed machine learning, computational VVUQ (verification, validation, and uncertainty quantification), experimental device testing, cardiovascular (patho)physiology, and strong and
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of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control
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, model-based and data-driven fault detection and identification, moving horizon estimation, convex optimization, randomized algorithms, stochastic programming, machine learning. In addition, excellent
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Requirements You should have the following qualifications: A strong background in machine learning. Knowledge of Bayesian optimization, Gaussian processes is a plus. Background in mechanics is highly desired. A
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with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software, signal
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opportunity to learn a lot and contribute to the next generation of machines that will improve the assembly speed and reduce the environmental impact of the production of hundreds of billions of future chips