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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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(advantageous but not required) experience in electrochemistry or photoelectrochemistry experience in materials synthesis and characterization familiarity with operando or in situ techniques basic programming
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biosensors or optics. Should be comfortable being hands-on. Requires experience in circuit design and microcontroller programming. How to apply Apply for this scholarship at the same time you apply
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environment on regional development consisting of economic geographers and will also be affiliated with the interdisciplinary research programme “A Promised Land?” at the Centre for Regional Science (CERUM
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, computer science, or a closely related discipline (typically first-class or high 2:1, or equivalent; Master’s welcome) • Strong programming skills (for example Python, MATLAB, C/C++) • Strength in at least two of
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knowledge in physical chemistry and surface science. Pre-knowledge on programming and molecular simulations (MD or MC) are desirable. Only home (UK) students are eligible for the full funding, but
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Modern doctoral education with a structured course programme and skills courses in teaching, academic writing, and presenting Guaranteed funding for the duration of the programme Stimulating research
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listed here is subject to change without notice. Where we have listed information about jointly run scholarships programs, please also see our partners' websites. Information describing the number and
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Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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information . Starting in October 2026. You will need to meet the minimum entry requirements for our PhD programme . This project will allow the student to acquire highly transferable skills in probabilistic