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, moisture‑related defects and narrow processing windows, limiting their wider adoption. This PhD will address these challenges through a combination of experimental materials science, advanced
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: Semiconductor processing or microfabrication Computational modelling (COMSOL, MATLAB, or similar) Experience with optical/THz systems is beneficial but not essential Curiosity, problem‑solving ability, and
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, this enables the development of more accurate computational models for various industrial processes. The experimental methods developed in this project will directly contribute to a better understanding of
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an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical engineering, computer science & IT
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engineering, materials science, chemistry or a related discipline, with enthusiasm for experimental research and computational modelling. Excellent communication skills and the ability to work collaboratively
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and brain. Perform molecular, cellular and histological experiments, including AAV injections, tissue processing, immunostaining and stereological confocal image analysis. Develop and apply image and
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for applicants with a degree in Computer Science, Mathematics, Physics, or Engineering. Prior experience in AI is necessary. Prior experience in tomographic imaging and medical physics would be advantageous but
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extraction materials. Addressing these challenges requires fundamentally new approaches that integrate mineral liberation, molecular recognition, and process engineering. This PhD project contributes
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of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences). Who we are looking for An enthusiastic, self-motivated, resourceful
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Mobile Edge Computing (MEC) has emerged as a promising computing paradigm to support emerging high-performance applications by deploying resources at the network edge. However, most existing MEC