579 assistant-professor-computer-science-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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will make water networks robust and resilient to these threats. Candidate requirements Starting October 2026, we require an enthusiastic graduate with a 1st class degree in engineering, maths or a
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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in collaboration with Anglian and Northumbrian Water and their stakeholders. Ultimately, the findings will provide strategic evidence for the development of long-term adaptation pathways that will help
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the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex. Only UK domiciled applicants who meet the following criteria may apply: Not already have a Master’s degree And Be
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the average values of the widths and spacings between two adjacent resonances. This energy range is called the “unresolved resonance region” (URR). Current computational methods treat the resonances in the URR
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Dr Prachi Bhatnagar’s research team in the Population Health Sciences research institute, benefitting from the incredibly strong public health, sustainability and urban development networks
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reference). Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s). International applicants must also provide a valid
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sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong
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Project Overview This is an opportunity to conduct fully funded interdisciplinary research under the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex
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. This project will rely heavily on computational analyses, so experience on bioinformatics/informatics is required. The student will receive extensive training in big-data analysis, genome-wide/species-wide