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therapeutic and rehabilitation patient monitoring, through the implementation of software platforms and predictive models based on Artificial Intelligence and Machine Learning techniques. Developing solutions
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graphics. Classical representations are relatively easy to render, while being difficult for generative machine learning models. A recent breakthrough in this area is the Neural Radiance Field (NeRF) and
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(HAC). This role focuses on applying advanced computational and analytical methods—including artificial intelligence, machine learning, deep learning, time-series modeling, and large language models
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that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD project will: Investigate the multi-stage modelling
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-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
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, current technology in classroom, distance learning and laboratory environments creating and modeling a quality learning environment that supports a diverse student population preparing, distributing and