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system with integrated sensors. You should hold or be near completion of a PhD/DPhil with relevant experience in the field of robotics, biomedical engineering, information engineering, electrical
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experience: Essential criteria PhD in computer science, AI or related area Proven experience of knowledge engineering and semantic technologies such as knowledge graphs, ontologies, data modelling, RDF, RDFS
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of climate science, statistical methodology, and computational modelling. The PROTECT project is part of the ARIA-funded programme on climate tipping points and aims to improve the scientific and statistical
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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robotics, mechanical engineering or a similar engineering field. The job requires an in-depth knowledge about soft inflatable/eversion robotics, sensor data acquisition and processing, computer and system
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We are seeking a creative and highly motivated postdoctoral researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious
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experience: Essential criteria 1. PhD in computer science, AI or related area 2. Proven experience of knowledge engineering and semantic technologies such as knowledge graphs, ontologies, data
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project is time limited and will end on 31/12/2027. Person Specification Essential Criteria: Qualifications A good first degree in an Engineering related subject. A PhD (or be close to submission) in Marine
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scientific discipline. Experience in developing and running an atmospheric transport model. Experience in interpreting data. Experience with FORTRAN and Python computer languages. Proven ability to work
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying