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Bioinformatician

London
Queen Mary University Of London
Posted: 16 August
Offer description

For all our roles, we are seeking individuals passionate about contributing to cutting-edge cancer research and AI-driven innovation. Candidates should have a technical background, a collaborative mindset, and a commitment to research excellence and societal impact.

The project is based at the Barts Cancer Institute, part of the Faculty of Medicine and Dentistry.


About the Institute

The Barts Cancer Institute (BCI) is a Cancer Research UK Centre of Excellence dedicated to transforming lives through innovative research in laboratories, clinical settings, and populations. BCI is renowned internationally for its work in cancer research, combining basic research with clinical expertise. The institute supports the development of future cancer researchers through extensive postgraduate training.


About Queen Mary

Queen Mary University of London values diversity of thought and opinion, fostering an environment where interdisciplinary collaboration leads to innovative ideas. We offer flexible working arrangements, inclusive employment policies, and comprehensive staff benefits. Our commitment to diversity and inclusion is integral to our recruitment process, with reasonable adjustments made for candidates with disabilities.


Role Requirements

* PhD in a relevant discipline such as Bioinformatics, Computational Biology, or Computer Science.
* Proven experience in machine learning, AI, large-scale data analysis, and scripting languages.
* Ability to work effectively in a multidisciplinary team.

Join the ground-breaking PharosAI initiative—a £43.6M national programme co-led by Queen Mary University of London, aiming to revolutionize AI-powered cancer care and accelerate therapeutic development across the UK. This role offers a unique opportunity to contribute to building a pioneering cancer AI ecosystem, supporting NHS delivery, AI-driven drug discovery, and clinical applications. Be part of a visionary project that promotes data democratization, patient involvement, and education in AI for healthcare professionals.

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