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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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quantitative data analysis methods such as machine learning and behavioural classification in ants is essential. You will be ideal for this position if you are well-organised, eager to learn new skills and
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researcher who has recently completed a PhD in Chemistry, Biochemistry or close to completion or related field and expertise in computational chemistry, molecular simulation and machine learning. This project
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join the Physical Mathematics Laboratory, a team of researchers led by Dr. Stuart Thomson with interests in physical applied mathematics, machine learning, and robotics. The successful candidate will
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structure prediction tools. This will include development and application of interactive molecular dynamics simulations in virtual reality, and use of design methods based on AI and machine learning. You will
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well as their documentation. Proven experience in the development of image processing and computer vision methods such as visual descriptors, motion descriptors, activity recognition and/or machine learning. Proven experience
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activities as required. A PhD in statistics, machine learning, mathematical modelling or other relevant quantitative subject. Experience in infectious disease mathematical modelling. Experience with
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or computer modelling of behaviour. You will be highly motivated, collaborative and an excellent communicator (especially with respect to writing), and have a demonstrable desire to learn new skills
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of machine learning techniques to build predictive models for outcomes such as joint replacement; Mendelian Randomisation studies utilising genetic data in UK biobank and other cohorts to establish whether