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optimization of GRAS microorganisms at 1-10 L fermentation scale. ii. Design and optimise downstream bioprocessing for protein recovery. iii. Perform characterization of protein isolates for incorporation
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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calculations. In this collaboration, the experimental work will be carried out at the University of Salford, whereas the mathematical analysis and numerical modelling will be done at Nottingham. The work will be
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of plant invasiveness, mutualisms among invading organisms in forests, risks and benefits of non-native trees in forestry, social perceptions of non-native species in forests, economic optimization of forest
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essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into ¿human in the loop¿ workflows. This post is available for two years. Keywords: Geometric Deep
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. the project will be delivered in partnership with colleagues from both the professional service and academic communities. The work will involve linking numerous large student education datasets to local student
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essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows. This post is available for two years. Keywords: Geometric Deep
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holidays and five university closure days including closure between Christmas and New Year. Our reward scheme grants bonuses of numerous values for excellent work We are committed to staff development
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five university closure days including closure between Christmas and New Year. Our reward scheme grants bonuses of numerous values for excellent work We are committed to staff development through
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its future and associated space-weather hazard over the next 20 years. You will have a PhD (or close to completion) in geophysics, physics, applied mathematics or similar highly numerical discipline