110 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation" Postdoctoral positions at University of Oxford in United Kingdom
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professional development opportunities Before submitting an application, please review the full details of this post including the selection criteria by opening the 'Job Description' attachment below. To submit
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development to work under the supervision of Dr Alistair Farley, Scientific Lead for Chemistry, with a dotted line to Professor Timothy Walsh. The position is based at the Ineos Oxford Institute, at the Life
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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collaboration with the study team, and develop new statistical frameworks for evaluating multiple correlates of protection in vaccine trials and real-world evidence studies. Other duties include contributing
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vaccine platforms. You will provide support in the insectary work, and the related in vivo experiment and research work including research design, methodological development, and the analysis and reporting
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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drug and disease research in multiple projects in the group. The candidate is expected to lead a drug development project and support other group projects. This will include lab experiments, analysing
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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key