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well as academic guidance and mentorship. The scholarship may be awarded to applicants who have just completed their undergraduate studies; or have completed a Bachelor’s degree or a Master’s programme and/or have
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predictive checking, model comparison) • Computational modelling with Python and Dynesty, JAX, NumPyro, and PyTorch • Use of asteroseismic and spectroscopic survey data (e.g. PLATO, Gaia, APOGEE, TESS) • High
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opportunities. Be mentored by internationally recognised research leaders Our cohort-based, 4-year scheme offers a distinctive PhD training experience. During this structured PhD programme, you will complete
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across different imaging devices, including future sensors with unknown spectral sensitivities. Training The student will be based at the Colour & Imaging Lab at the School of Computing Sciences which has
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Trinity Laban Conservatoire of Music and Dance | London, England | United Kingdom | about 1 month ago
Introduction Trinity Laban is accepting applications for a new PhD fund in partnership with the Satyam Kalon Programme. The Programme is a ground-breaking collaboration between academics, artists
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experience to become a rounded scholar: £20,780 annual maintenance grant* Full coverage of PhD programme fees Higher Education (HE) teacher training and practice. This means that as well as pursuing your PhD
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Honours degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work
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associated with genome dominance. Transposable element distribution and activity will also be analysed using long-read sequencing. This project is primarily computational and offers comprehensive
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About Us The applicant will join the new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap