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Field
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fraction of the data we collect. In this project, we will therefore seek to use advanced mathematical and machine learning approaches to identify hidden information within cardiac electrical signals that is
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(neural networks, model training and evaluation). Graph neural networks (GNN) or other current architectures (e.g. transformers). Causal inference or computer networks. Interpretability of AI (mechanistic
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The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology
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imperial conquest; the intersection of military conscription, coerced service, and slavery; slavery's role in the logistics, supply chains, and infrastructure of ‘Long Nineteenth Century’ armies
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degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning
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Shahab Resalati Supervisors: Dr Aydin Azizi Contact: Prof Shahab Resalati ([email protected] ) Requirements: Entry requirements: Essential Criteria Master’s degree (or equivalent) in Electrical
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sensing system for simultaneous time-domain measurements of voltages, currents, electric fields and magnetic fields, supported by digital signal processing. Using these synchronised measurements, you will
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Are you a MSc graduate with background in data science, computer science, biostatistics, bioinformatics or a related field? Do you have a solid foundation in machine learning? Are you passionate
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of Electrical Engineering at TU/e is a globally recognised, leading scientific and applied research group focused on exploiting light for communication and quantum systems. We apply our knowledge in collaboration
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aviation sector is undergoing a once-in-a-generation transformation towards cleaner, more sustainable flight. Cryogenically cooled electrical machines are of significant interest for the future of air