113 algorithm-development-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" Postdoctoral positions at University of Oxford in United Kingdom
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We are seeking a Postdoctoral Research Associate to support our projects to understand membrane evolution. The aim of this project is to use molecular dynamic simulations to understand membrane
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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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analysis of data from a Nipah virus vaccine trial, using machine learning and statistical tools to identify immune response markers for future trials. You will be responsible for developing new and adapting
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12 months. The project involves developing nanopore sensing technologies for functional biomolecules and is funded by Bill and Melinda Gates Foundation. Find out more about the research and group
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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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in the Department of Chemistry, University of Oxford, for a period of up to 3 years. The project involves the development of methods to use light to regulate transport of amino acids and to engineer
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form, how it is generated and how it evolves. In particular we focus on the evolution and evolvability of vertebral counts, and we use various species of Lake Malawi cichlids as our model organism
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to advance hepatitis B and liver disease research and to develop novel analytic approaches for NHS routine data. You will be based at the University of Oxford, joining a collaborative, interdisciplinary