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Field
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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high-quality research in one of the Department's key research areas: (i) Artificial Intelligence and Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv) Distributed Systems and Networking. These key research areas have a special thematic focus on (a
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and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and social network analysis. Machine learning with
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research