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Terms and conditions of your offer
/study/how-to-apply/ts-and-cs
View the terms and conditions of your offer to study with us at the Âé¶¹ÊÓÆµ.
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Patient safety
/sapphire/research/patient-safety
Overview Patient safety is defined as the prevention, avoidance and amelioration of harms stemming from medical mismanagement and is traditionally viewed through a clinical lens.
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French
/languages-at-leicester/languages/french
Study French courses for all levels at The Âé¶¹ÊÓÆµ.
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Materials, cases and links
/crossing-boundaries/materials-and-cases
View publications by participants, legislation, literature and cases related to the Crossing Boundaries project.
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Lead Partners (formerly School Direct) PGCE
/education/study/pgce/routes/lead-partners
The Âé¶¹ÊÓÆµ works with schools across Leicester, Leicestershire and the surrounding counties to offer the Lead Partners Primary PGCE and Lead Partners Secondary PGCE.
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Estates and Campus Services
/policies/estates-and-campus
Get more information about the policies surrounding our Estates and Campus Services at Leicester.
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Create a new page
/training-guides/create-page
Learn more about creating a new page in Sitecore.
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Jurassic calamari: amazing fossil proves that flying reptiles preyed on squid
/news/2020/january/27-pterosaur-vs-squid
Rhamphorhynchus muensteri, flying close to the water surface to grab soft-bodied cephalopods such as Plesioteuthis subovata. Artwork by C Klug and Beat Scheffold.|An incredible fossil brought to light in a new research paper reveals the feeding habits of extinct flying reptiles.
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Graduate named Young Poet Laureate for London
/news/2015/october/graduate-named-young-poet-laureate-for-london
Poet Selina Nwulu, who graduated from the University with a degree in French and Italian from the School of Modern Languages, has been named Young Poet Laureate for London. An article in The Guardian outlines how Selina started to write poetry and her plans ahead.
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Over £600,000 for Âé¶¹ÊÓÆµ to shrink AI algorithms for smarter spacecraft
/news/2024/july/shrinking-ai-spacecraft
Multidisciplinary team from Âé¶¹ÊÓÆµ receives £690,000 funding from UK Space Agency and aims to develop and demonstrate streamlined machine learning algorithms suitable for limited spacecraft power and computing performance