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Field
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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mathematics, applied physics, or a closely related field. Good theoretical understanding of the fundamentals of machine and deep learning, with a strong interest in methodological development rather than only
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. • Karimi H. et al., “Wavelet Based Protection of Microgrids”, IEEE Transactions on Smart Grid, 2019. • Heidari A. et al., “Deep Learning for Fault Detection in Smart Grids”, Applied Energy, 2021. • Wen L. et
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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will work with deep learning, affective computing, multimodal signal processing, graph neural networks, hypernetworks, temporal modelling and responsible AI. Expected outputs include personalised
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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federated learning and multimodal deep learning models for healthcare. The project will focus on enabling privacy-preserving learning from distributed healthcare data sources, including longitudinal medical
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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for the position. Preferred selection criteria Experience or strong interest in one or more of the following areas is considered an advantage: Machine learning, deep learning, natural language processing or data