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to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. Involved in supporting an electrophysiology-based machine learning model
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passionate plant science researchers, bioinformaticians or remote sensing/data scientists with skills in image processing or phenotyping with a collegiate and self-driven attitude towards multidisciplinary
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to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. ii) Involved in supporting an electrophysiology-based machine learning
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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the university, genomic and metabolomic measures, offering novel potential to explore the physiological basis for imaging measures and apply machine learning in a radiological context. You will join an established
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imaging platforms and computer programming. The successful applicant will have an MSc or postgraduate degree in a relevant topic, relevant experience in AI-based imaging platforms and computer programming
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or fracture modelling, or equivalent experience Robust experience with computer coding (e.g., Python, C++) Track record of publication, commensurate with career stage, in internationally recognised journals