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Field
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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circular consumption contributes to extending product lifetime, retaining material value, and reducing waste, with cases on e-bikes and electronics (washing machines). Using a transdisciplinary research
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
fields Experience with image analysis, or computer vision Good knowledge of basic machine learning techniques, such as variational autoencoder Good presentation and writing skills Proactive, independent
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selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
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as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the project Can AI interpret graphs like a human materials scientist
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or equivalent High competence in machine learning and robotics Knowledge and experience in computer vision is an advantage Good mathematical and programming skills (Python and C/C++; ROS is an advantage) Good
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or equivalent High competence in machine learning and robotics Knowledge and experience in computer vision is an advantage Good mathematical and programming skills (Python and C/C++; ROS is an advantage) Good
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to extending product lifetime, retaining material value, and reducing waste, with cases on e-bikes and electronics (washing machines). Using a transdisciplinary research approach that includes stakeholder
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mathematics, machine learning, photonics, and clinical practices in vision. Be part of a multidisciplinary research team spanning science and engineering, psychology, and healthcare. Access state-of-the-art
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning