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in a discipline related to “Greenhouse gas emissions modelling; Reduced-complexity modelling of the land surface carbon cycle; Climate modelling; Climate mitigation modelling”; demonstrate expertise in
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and experience required to perform the role are an aerothermal PhD with a combination of experimental, numerical and low order modelling experience. The PhD must be completed or near completion, and
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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analyse the experimental data, developing techniques to extract information about the rotational orientation dependence of the gas-surface reaction, as well as performing numerical simulations to determine
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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work with fish behavior, engineering geology, turbine design, variable speed operation, etc. It involves fundamental research including a literature study and CFD modelling with the use of real case