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on: • In-depth expertise in reliability testing of wide bandgap (WBG) technologies • Deep knowledge in in-situ measurement techniques for WBG technologies • You work on developing hybrid prognostic
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Intelligence (AI). Despite recent advances in data-based AI (e.g., deep learning and LLMs), knowledge-based methods are still state-of-the-art when it comes to building reliable and explainable decision support
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This fully funded PhD position focuses on Sustainable Space, addressing critical challenges such as orbital debris management, space traffic optimisation, or deep-space exploration. Your research will
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background in machine learning, including Natural Language Processing. You have excellent coding skills in Python; hands-on experience in deep learning frameworks such as PyTorch or Tensorflow is a plus You
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interest in machine learning, deep learning, and data analytics with a focus on applications in the life sciences. You have excellent written and verbal communication skills in English, have an inquisitive
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combine EMI footprints, which capture normal variations through characteristic curves and statistical distributions, with state-of-the-art machine learning and deep learning techniques (e.g., one-class
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the patient, the influence of the ceiling shield, and the worker surrounded by the radiation field. A deep learning approach using a Convolutional Neural Network (CNN)-Architecture could replace the
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Job description PhD position in computational neuroscience - Deep reinforcement learning closed-loop control for the treatment of epilepsy As part of the highly prestigious ERC Starting Grant