Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Ludwig Oxford Director Professor Xin Lu features in this showcase of women in diverse roles across Oxford’s Medical Sciences Division.

100 years since women were admitted as full members of the University of Oxford, women now hold vital posts at all levels of this institution. Across the 16 departments and numerous affiliated units, the women of Medical Sciences come from all walks of life, from all backgrounds and from all over the world.

The Medical Sciences Division asked 100 of these women to take part in a project to showcase the diversity of their roles, ranging from researchers, professors, and students through to administrators, receptionists, fundraisers, lecturers and clinicians.

Ludwig Oxford Director Professor Xin Lu is included among this group of incredible women working across the Medical Sciences Division, each reflecting on their journeys, their place in Medical Sciences and their vision for the next 100 years.

Each woman featured represents countless more working in our labs, in our offices, in our lecture theatres to make the Division, the University and the medical sciences a better place.

Find out more on the Medical Sciences Division website.

Read Xin Lu’s entry in the 100 Women of Medical Sciences.

Similar stories

Vacancy - Postdoctoral Researcher - Cardiotoxicity

Grade 7: £34,308 - £42,155 with a discretionary range to £46,047 per annum. Apply by 24 October.

Mathematical methods for analysing single-cell transcriptomic data

An interdisciplinary collaboration has resulted in the development of three multiscale data methods for the analysis of single-cell transcriptomic data that have advantages over current methods.