Research Associate*/Research Assistant in Multi-omics Early Detection of Ovarian Cancer (Fixed Term)

An exciting opportunity has arisen for a highly motivated Research Associate to join the Centre for Cancer Genetic Epidemiology (CCGE) at the Department of Public Health & Primary Care, University of Cambridge, to contribute to a new international study: the NEMO (Novel Early Markers of Ovarian Cancer) study, funded by Cancer Research UK's Alliance for Cancer Early Detection (ACED).

NEMO is a multi-centre initiative aiming to identify early molecular signatures of pre-cancerous changes in the fallopian tubes that precede the development of high-grade serous ovarian cancer (HGSC), the most lethal form of ovarian cancer. The study will deploy state-of-the-art technologies including single-cell genomics, spatial transcriptomics, proteomics, and glycoproteomics, to characterise tissue and lavage samples at an unprecedented resolution.

The postholder will be based at the CCGE in Cambridge, with co-mentoring from the MRC Biostatistics Unit, and will work closely with a multidisciplinary team of international collaborators across the ACED network, including UCL, the University of Manchester, Stanford University, and OHSU.

The successful candidate will be expected to lead the development and application of analytical approaches for the integration and interpretation of multi-omic datasets. This includes identifying molecular networks, predictive features, and signatures of early disease. The work will involve close collaboration with experimental scientists, bioinformaticians, and clinical researchers across partner institutions.

Applicants should have:

• A PhD in a relevant field e.g. genomics, computational biology, systems biology, bioinformatics, or related discipline.
• Demonstrated experience in analysing large-scale biological data (e.g. single-cell data, spatial or bulk omics);
• Strong programming and statistical skills, ideally with proficiency in R and/or Python;
• Experience in data integration, network analysis, and/or machine learning applied to biological questions;
• A collaborative mindset, excellent problem-solving skills, and a strong publication record;
• Excellent written and oral communication skills

The CCGE are committed to supporting hybrid working, but staff are expected to work onsite on a regular basis to foster collaboration and community.

This is a full-time position. We also welcome applications from those wishing to work part-time of no less than 0.6 FTE per week.

Funding is available for 2 years from commencement in post.

To apply online for this vacancy and to view further information about the role, please click 'Apply' above.

Applicants must have (or be close to obtaining) a PhD. 

Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will initially be appointed as a Research Assistant (Grade 5, Point 38 £34,132) moving to Research Associate (Grade 7) upon confirmation of your PhD award.

Please ensure that you upload a covering letter and CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

Closing date: 25 September 2025

Interview date: 6 October 2025

Please quote reference RS46925 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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