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  • Geology PhD students

    Browse the current PhD students in Geology within the School of Geography, Geology and the Environment at the University of Leicester, and see their contact details and research topics.

  • Emoji is the fastest growing language

    Posted by Andrew Dunn in Social Sciences and Humanities Librarians’ Blog on June 19, 2015 Emoji’s are ‘pictographs. Originally used in Japanese electronic messages, many characters have now been incorporated into Unicode  and the launch of Emoj.li.

  • Investment Management

    Module code: AF3077 Investment management focuses on how companies and firms can meet their investment goals by buying stocks to public or private investors.

  • Investment Management

    Module code: EC3077 Investment management focuses on how companies and firms can meet their investment goals by buying stocks to public or private investors. This module will introduce you to trading in equity markets – the meeting point for buyers and seller- and bond markets.

  • Investment Management

    Module code: AF3077 Investment management focuses on how companies and firms can meet their investment goals by buying stocks to public or private investors.

  • Economics

    Find your research degree supervisor in Economics at Leicester.

  • Representing BAME individuals in Covid-19 research

    Researchers must ensure people of Black, Asian and Minority Ethnic (BAME) groups are proportionately represented in Covid-19 studies, according to scientists from UK and USA.

  • New study calls for action on air pollution in Northern India

    New research led by the University of Leicester calls for urgent action on open field burning to reduce the intensity of post-monsoon air pollution in Northern India.

  • Volcanology

    Our volcanology research covers volcanic eruptions and magmatic processes in many different parts of the world

  • Over £600,000 for University of Leicester to shrink AI algorithms for smarter spacecraft

    Multidisciplinary team from University of Leicester 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

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