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structure characterisation experience in a field research environment, working on large scale agricultural trials demonstrated experience in use of research computing with a deep understanding of modern data
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field, expert knowledge in at least one of robotic sensing and perception; learning and processing architectures; or mapping and knowledge representation, extensive experience with one or more programming
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to operate and maintain synthetic laboratory equipment ability to conduct or learn associated skills such as computational docking and biophysical assays. Base Salary The base salary will increase in July 2025
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to identify and remove biases and barriers in an effort to make our workplace open, supportive and safe for everyone. To learn more about the School of Physics, click here About you The University values
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Carbon Modelling. This position offers an exciting opportunity to make use of the recent developments in AI and machine learning algorithms by measuring soil properties rapidly over a large area using cost
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written and verbal communication, record-keeping, and organisational skills ability and willingness to learn new techniques and optimise experimental approaches awareness of laboratory safety, and
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oscillations, delta Scuti stars, exoplanets, stellar clusters and associations, and machine learning. We make extensive use of data from NASA’s Kepler,TESS and JSWT Missions, and also have access to ground-based
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science and lipids. Experience with AI and machine learning is preferrable but not mandatory experimental skills on microfluidics, analytical techniques, microscopies (fluorescent, confocal and electron
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on the ground robot for real-time soil property assessment, providing critical below-ground data to complement above-ground phenotypic analysis develop machine learning algorithms for real-time analysis of plant
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approaches combined with live-imaging and multi-plex in situ hybridization to test regulatory hypotheses. There will be opportunities to work with and learn from the other three RESYDE interdisciplinary