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. This is part of an EPSRC-funded project on Algorithmic Comparison of Stochastic Systems. The post holder will work closely with the Principal Investigator, Stefan Kiefer, but also with other members of a
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on the robustness (continuity) of equivalences in probabilistic systems. This is part of an EPSRC-funded project on Algorithmic Comparison of Stochastic Systems. The post holder will work closely with the Principal
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Job description: Are you passionate about protecting the natural environment and who enjoys working collaboratively? We have an exciting opportunity to join us at the University of Sheffield, in association with Detectronic Ltd a UK leader in environmental monitoring technology. We are...
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Overview The University of Sheffield, in association with Detectronic are recruiting a Knowledge Transfer Partnership (KTP) Associate to help develop and test a groundbreaking monitoring system for continuous water quality measurement in rivers. This is in response to the national need to...
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Are you passionate about protecting the natural environment and someone who enjoys working collaboratively? We have an exciting opportunity to join us at the University of Sheffield, in association with Detectronic Ltd a UK leader in environmental monitoring technology. We are recruiting a...
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Join Oxford Digital Health Labs as a Senior Research Scientist or Software Engineer, where you will play a pivotal role in developing advanced algorithms for cardiotocography and fetal ECG analysis
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Join Oxford Digital Health Labs as a Senior Research Scientist or Software Engineer, where you will play a pivotal role in developing advanced algorithms for cardiotocography and fetal ECG analysis
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the European Research Council (ERC). The project aims to tackle central challenges in the fields of online algorithms and learning-augmented algorithms. Goals of the project include the advancement
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for monitoring offshore renewable energy. Large datasets collected across active and passive acoustics, sometimes supported by camera imagery, require development of robust and reliable algorithms to automate
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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed