707 computer-science-quantum "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" PhD positions in United Kingdom
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. The goal of this PhD project is to develop a novel account of what it means to learn from error in science. In what sense is science “self-correcting”, and what does this process entail? How
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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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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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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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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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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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provide considerable training in chronobiology, signal transduction in plants, microbiology, and data analysis and interpretation. As part of the “MicroClock” programme funded through an ERC Synergy grant
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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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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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. 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