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difference in the areas of materials and manufacturing, we look forward to receiving your application. About the research project The position is focused on development of robust AM and post-AM processing
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interdisciplinary research activity, and contribute to grant development aligned with your research interests. Teaching support may be required, up to a maximum of six hours per week. About you You will have a PhD
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maximum of six hours per week. About you You will have a PhD (or near completion) in a relevant discipline (e.g. geography, GIS, data science, public health, or a related field) or equivalent research
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newly funded research centre that aims to conduct world class research that contributes to a world where differences between people are understood and accepted, strengths are capitalised on, and mental
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, plant and planetary health. The microbial diversity in different niches is enormous, and it is not all bacteria or viruses that are harmful and many are even beneficial. We are constantly faced with
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quantum networks, where one bit of information is encoded in the quantum state of a single photon. You will be part of a team of 10-12 people between senior staff, PostDocs and PhD/Master students
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. The PDRA will quantify the differences in calculated and measured experimental conditions by adapting the Geodetic Bayesian Inversion Software ( https://doi.org/10.1029/2018GC007585) ). Working alongside our
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teaching are conducted within seven divisions with different research focus. Read more about us here About the division The Division of Biomedical Engineering is part of the Department of Materials Science
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inequalities in test access (QUEST). Each project is at a different stage in its development and so there is an opportunity to contribute to answering more established research questions, as well as helping
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this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert knowledge of deep