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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
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. Familiarity with machine learning and proficiency in Python or MATLAB. Excellent communication skills; proficiency in Dutch is desirable but not required. The capacity to thrive in a complex and dynamic
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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment
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to applicants with published research or exceptional academic performance. Experience in computer vision or audio processing is desirable and will be considered an advantage. A doctoral candidate is expected
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1
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partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites