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advanced AI/ML methods to neural and behavioural data to uncover the computational foundations of decision-making in humans and animals. Designing novel AI algorithms inspired by brain function, to improve
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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record in astrocyte-neuron interaction modelling, published in Frontiers in Computational Neuroscience, PLoS Computational Biology, Neurocomputing, and IEEE Transactions on Neural Systems and
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explore how this programme could also work for children with SEN and complex needs, and engage parents and families more directly. Aim: To explore the impact of a school based PL programme for children with
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opportunities to be active. There are a number of PL programmes to support physical literacy and motor skills in children, however, the next step is to explore how this programme could also work for children with
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through the vessels and how diseases like heart blockages or artery wall damage develop. However, most current computer models used to study blood flow treat arteries as if they are rigid and motionless
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: Strong programming skills in Python (NumPy, pandas, PyTorch or TensorFlow) Machine learning fundamentals and algorithmic thinking Ability to design and implement computational experiments Clear technical
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Summary Positioned within Ulster University’s School of Computing, this research theme explores the integration of artificial intelligence with quantum technologies to advance next-generation
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vision systems addressing an urgent industrial challenge with immediate, large-scale impact. The project will take existing knowledge in computer vision and deep learning and apply it directly to a
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computational fluid dynamics (CFD) and computer-aided design (CAD) software. They should also be prepared to engage in both computational analysis and experimental testing as required. Essential criteria