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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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laboratory, including the development of SOPs and BIOCOSHH forms The Person Knowledge, Skills and Experience Ability to work well as part of a team and rapidly acquire new skills Detailed knowledge of innate
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researchers in applied mathematics and machine learning. This is due to its remarkable flexibility, mathematical elegance, and as it has produced state-of-the-art results in many applications. As a leading
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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successful in this role, we are looking for candidates to have the following skills & experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation
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backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute
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-quality academic articles and publish them in internationally recognised, reputable journals. You will mentor and co-supervise PhD students affiliated with the project. You will assist with project-related
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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computational biology and mathematical spatial analysis via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics, computational biology, data
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will also develop skills in mathematical modelling and spatial analysis via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics