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Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
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Overview About the Role Applications are invited for a motivated and committed Machine Learning Engineer. The successful candidate will contribute to Queen Mary’s national reputation for research by planning
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machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
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). Please also provide information for two referees (this does not have to be your PhD supervisor but should be able to talk directly to your research). The position will involve working with machine learning
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genomics/transcriptomics, computational biology and machine learning. The overarching aim of the project is to develop strategies to treat patients with rare genetic disorders at scale. Duties will include
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standards, and you will identify and troubleshoot technical or scientific problems, working collaboratively with database experts and scientific programmers. It is essential that you hold a PhD/DPhil in a
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Grade Grade 5 Salary £42,447 to £50,961 per annum Contract Permanent Term Full time - 37 hours per week Apply by 27 June 2024 23:55 Interviews tba Job description Senior Digital Learning Coordinator
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Arrangements: on campus with remote working when applicable Closing Date : 08-Jul-2024, 6:59:00 PM Disclosure and Barring Service Requirement: Not Applicable. The University At Durham University we are proud
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- 35 hours per week Apply by 24 June 2024 23:55 Interviews 3rd July 2024 Job description Staff Development Consultant (Digital Learning) JDPS (May 2024).docx – 125KB Converted File Staff Development