49 genetic-algorithm-"Multiple" Postdoctoral positions at University of Oxford in United Kingdom
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. The main objectives of Dr Kapetanovic’s research programmes are the: • Development of novel genetic and optogenetic therapies for retinal diseases. • Investigation of innovative ocular surgical techniques
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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close to completion of a relevant PhD. You will manage your own academic research, effectively coordinating multiple strands of work. Direct experience in molecular genetics and/or plant-microbe interactions
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We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new
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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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small-scale project management and to co-ordinate multiple aspects of work to meet deadlines. About you You will hold a PhD/Dphil (or near to completion) in molecular biology or cancer biology with
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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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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis