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interpretation of the data generated and that’s where this project comes in. You’ll be applying metagenomic techniques to respiratory samples and developing analysis and interpretation approaches that facilitate
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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relativistic quantum information (RQI). This highly challenging PhD experience will offer you the chance to work on world-class research combining quantum information theory with key aspects of quantum field
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, security, and/or machine learning are preferred. To apply, please submit the following documents to Dr Hai Dong (hai.dong@rmit.edu.au). Expressions of interest should contain the following information: A one
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Are you passionate about applying computational approaches to solve problems in biomedicine? We are now looking for an Industrial PhD student in Data-Driven Life Sciences to work on a cutting-edge
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Research Abstract: This project aims to address the challenges in robot inspection, which is one of the key applications in future robots. Often, as part of the inspection, robots collect various data
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Evaluating the use of longitudinal over-the-counter healthcare sales data as a source of syndromic surveillance for community-based infections UK Health Security Agency and Nottingham University
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at uncovering how the capacity for Darwinian evolution may have first arisen. While parts of this puzzle have been solved, an important open question is the emergence of information transfer (inheritance), one
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interpretation of the data generated and that’s where this project comes in. You’ll be applying metagenomic techniques to respiratory samples and developing analysis and interpretation approaches that facilitate
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department’s activities here: https://www.uu.se/en/department/immunology-genetics-and-pathology Read more about our benefits and what it is like to work at Uppsala University The Data-driven life science (DDLS