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focus on processing and utilising machine-learning techniques to analyse large volumes of data from sensors installed in Phase 1. The aim will be to merge the QC points and tracking system developed in
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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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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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, machine learning, and causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and
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, economic growth, inequality, and culture. The project draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning
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draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning to assess the impact of colonisation. About you PhD in
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methodology. Experience in large data sets and their platforms/tools, cloud-based architectures, and deployment frameworks for machine learning algorithms. Experience in Deep Learning techniques and solutions
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Associate to undertake fundamental and applied research into novel deep learning approaches to analysing LiDAR and vision data in forest imaging and remote sensing. The role is part of a three-year project