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the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process
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vivo imaging platforms. Key responsibilities To work within the Mansour and Roghanian labs, and with collaborators at Southampton and beyond. To design, conduct, and analyse in vitro and in vivo
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of installation. Your work will include the development of advanced numerical models of the transition system, alongside the testing of a scaled prototype structure to demonstrate proof of concept. You will lead
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models that predict post-fabrication device characteristics from process parameters. You will engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy
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well as international collaborators to lead scientific projects on AGN identification, and measurement of supermassive black hole and host galaxy masses using imaging and spectroscopic data from ground and space. The
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it can be managed sustainably. The role The post is funded by the Leverhulme Trust as part of the project “Quantifying the socio-economic consequences of losing bat guano services”, led by Dr Veronica
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development will be combined with a large-scale wafer process with industrial-level uniformity and spatial resolution by using the pilot 300-mm semiconductor process line available in collaboration with AIST
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software framework for the testing of age estimation technologies (AET) from facial images This is an exciting opportunity to contribute to cutting-edge biometric research at the intersection
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, materials development and processing, or thin-film characterisation. Ability to work collaboratively within interdisciplinary research teams. Good communication and interpersonal skills. Desirable
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including anechoic chambers and controlled experimental environments, ensuring high-quality, repeatable data. Develop and apply signal processing workflows for weak transient signals, long-term monitoring