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-verification, and in-country assessments of the nature, extent and likelihood of threats to these heritage sites. MAEASaM is a partnership programme, with the University of Cambridge serving as the lead and
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services research and epidemiology, along with practical skills in managing large, complex datasets. Proficiency in statistical software, especially STATA, is essential. You must be able to handle multiple
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Liverpool where, in the School of Computer Science and Informatics, we have an active group of PhD students, postdocs, and academics working at the intersection of Machine Learning, Verification and
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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autonomous sensing systems. The project requires experience with setting up sensing hardware, software defined radio systems and other lab equipment for data collection in addition to experience of programming
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systems (e.g., GC) Experience in synthesis and characterization of heterogeneous catalysts Hands-on experience with (pressurized) chemical reactors Experience with modelling and simulation software (e.g
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data science and machine learning Additional software skills such as Shiny, LaTeX, Tidyverse, Tableau, C/C++, Java, GitHub Experience in report writing for projects underpinned statistics Attributes and
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simulations to improve on existing power usage models. This research will be a key component of making computing more sustainable by providing novel insights into the energy usage of scientific software and
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to protect our planet from asteroid hazards. The aim of the project is to deliver and validate of an onboard astrodynamics GNC software for autonomous proximity operations around minor celestial bodies
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• Skilled in prototyping and experimental investigations for marine vehicles • Skilled in using mainstream CFD and autonomous system softwares (e.g. Star-CCM+, OpenFoam) • Research experience in