Job Description

Job number

MED05440

Faculties

Faculty of Medicine

Departments

Institute of Clinical Sciences

Salary or Salary range

49,017 - 52,922 per annum

Location/campus

Hammersmith Campus - On site only

Contract type work pattern

Full time - Fixed term

Posting End Date

15 Oct 2025

About the role

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Applications are invited for a 4-year postdoctoral research position to work collaboratively between the teams of Alexis Barr (MRC LMS) and Maria Secrier (Dept. of Genetics, UCL). We are seeking a talented data scientist to deliver an innovative and exciting research project investigating the differences and similarities in cell cycle dysregulation between different cancer types. For more information please visit https://www.barrlab.com and https://secrierlab.github.io/ or contact Alexis Barr (abarr@ic.ac.uk) and Maria Secrier (m.secrier@ucl.ac.uk).



The Barr group studies the mechanisms that control entry into and exit from the cell cycle, with a focus on quiescence entry and exit. A major goal of our research is to understand how these mechanisms become dysregulated in cancer cells, how that drives tumorigenesis and how we can target these dysregulated mechanisms to halt cancer cell proliferation. The Secrier lab investigates how mutational processes and cell-state transitions drive cancer development and progression, and how the tumour microenvironment shapes these dynamics, with a particular focus on tumour dormancy. To tackle these questions, we develop and apply advanced statistical modelling, data integration, and machine learning methods across large scale multi-omics datasets. The Barr and Secrier teams have successfully worked together over the last five years, leading to three joint publications, with more in the pipeline. This position will lead a new, CRUK-funded project between the teams to use bulk and single-cell tumour sequencing data to analyse and understand the mechanisms of cell cycle dysregulation across cancer types to identify similar and distinct mechanisms. The successful candidate will work collaboratively with wet-lab scientists in the Barr lab who will experimentally test hypotheses generated and with clinicians to guide the translational aspects of the work.

What you would be doing

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The successful candidate will have experience of performing statistical analysis and integration of large-scale genomic datasets (bulk and/or single-cell) and will apply these skills to discover mechanisms of cell cycle dysregulation across cancer types. You will have access to state-of-the-art computational infrastructure, large multi-omics datasets, and close collaborations with wet-lab scientists and clinicians for experimental validation and translation.

What we are looking for

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A PhD in a field related to bioinformatics, computational biology, genetics, statistics or a similar discipline. Experience in statistical analysis and interpretation of large-scale genomic data A track record of research in a relevant field (eg bioinformatics, data science/ML, statistical genetics) Excellent programming skills in Python/R or other programming language Good knowledge of statistics Experience with data management, best coding practices and reproducible workflows Strong interest in cancer biology and evolution

What we can offer you

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You will have the opportunity to work at the cutting-edge of human mechanistic and translational research in a vibrant and supportive environment. You will be encouraged to contribute to other projects within the Barr and Secrier teams. There are excellent opportunities for professional development - taking full advantage of collaborations, facilities and informatic expertise across MRC LMS and Imperial College. We will provide training and mentoring to support your career aspirations.


The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity. Grow your career: Gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).

Further information

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This is a Full Time Fixed Term role (4 years), based at our Hammersmith Campus.

Available documents

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Attached documents are available under links. Clicking a document link will initialize its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.



If you encounter any technical issues while applying online, please don't hesitate to email us at support.jobs@imperial.ac.uk. We're here to help.


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