About us
The UCL Cancer Institute is one of the worlds leading centres for cancer research bringing together internationally recognised scientists clinicians and researchers to advance our understanding of cancer and improve patient outcomes. Situated in the heart of London the Institute combines cutting-edge laboratory research with strong clinical partnerships creating an environment where scientific discoveries can rapidly translate into real-world impact. This PhD studentship is part of the ReIMAGINELife programme a major initiative funded by Prostate Cancer UK that aims to transform prostate cancer diagnosis and risk stratification through the integration of molecular imaging and clinical data.
About the role
This fully funded four-year PhD studentship offers an exciting opportunity to explore how molecular characteristics present at diagnosis influence the long-term progression of prostate cancer. Working within the Treatment Resistance Team led by Professor Gerhardt Attard you will analyse and integrate large-scale multi-omics datasets including genomic transcriptomic epigenomic and proteomic data alongside imaging and clinical information. You will develop innovative computational approaches and predictive models to improve risk stratification disease monitoring and patient outcomes. The role provides extensive training in cancer genomics machine learning statistical analysis and scientific programming while working in a highly collaborative multidisciplinary research environment.
How to Apply
Submit the following by 23rd November 2026- (using the apply button.)
- CV including:
- Contact details of two referees (one academic).
- A short statement explaining how your experience aligns with the project and person specification.
- Academic transcripts and certificates (PDF format). Include official English translations if applicable.
Informal enquiries about this post (including arrangements to visit UCL Cancer Institute) are welcome and may be directed by email to Prof Gert Attard () and/or Dr Stefano Lise ().
About you
You are a highly motivated and academically outstanding graduate with a first-class degree (or equivalent) in bioinformatics computational biology computer science mathematics statistics physics engineering or a related quantitative discipline. You have experience in Python and/or R programming and possess strong analytical and problem-solving skills. You are excited by the opportunity to apply computational methods to address important biomedical challenges and have a genuine interest in cancer genomics and translational research. You thrive both independently and as part of a multidisciplinary team communicate effectively and are eager to learn new computational and analytical techniques that will contribute to impactful scientific discoveries.
The job does not meet the eligibility requirements for a Skilled Worker Visa certificate of sponsorship under UK Visas and Immigration legislation. Therefore UCL will not be able to sponsor individuals who require the right to work in the UK to carry out this role.
What we offer
This is a full time fully funded 4year PhD studentship successful candidates will receive a non-taxable annual stipend of 23805 pa covering tuition fees at Home rate and meet UCL PhD admissions criteria.
Stipend will go up to 24522 in year 2 25260 for year 3 and 26021 in year 4.
Our commitment to Equality Diversity and Inclusion
As Londons Global University we know diversity fosters creativity and innovation and we want our community to represent the diversity of the worlds talent. We are committed to equality of opportunity to being fair and inclusive and to being a place where we all belong.
We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCLs workforce.
These include people from Black Asian and ethnic minority backgrounds; disabled people; LGBTQI people.
Our division holds an Athena SWAN Gold award in recognition of our commitment to advancing gender equality.
You can read more about our commitment to Equality Diversity and Inclusion here:
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