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seeking applicants with experience in: Radial velocity, stellar, and/or exoplanet spectroscopy studies; and/or astronomical instrumentation or observing programmes Machine learning in physical science
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collection and management for research, as well as basic knowledge of both quantitative and qualitative data analysis principles. Demonstrate high levels of computer literacy, including proficiency in learning
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collection and management for research, as well as basic knowledge of both quantitative and qualitative data analysis principles. Demonstrate high levels of computer literacy, including proficiency in learning
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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics