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honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics. Experience in coding and CFD is advantageous but not mandatory—an eagerness to learn and innovate is key! Full training will
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no.: 4635 Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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project aims to explore techniques that could facilitate experts in the elicitation of priorities. One possible direction could be to use the technique of Inverse Reinforcement Learning (IRL) [2], [3]. IRL
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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projects. The role will also provide the opportunity to learn and develop medicinal chemistry skills as applied to drug design. The role is ideal for someone who has, or is about to be awarded, a PhD in
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters