About the role:

We are pleased to be able to offer the opportunity for a Principal Bioinformatician to join the NBC, to work on the optimisation and validation of our TOO classifier1. You will work alongside our multidisciplinary team of clinicians, molecular biologists, and computational scientists on a Cancer Research UK funded project to develop CUPiD towards a clinically validated approach.

The main area of focus will be the development and application of statistical/machine learning approaches to enhance classifier performance, for example through:

Refinement of data augmentation strategies for generating training data sets; Multi-omic data integration, combining genomic and fragmentomic features alongside methylation profiles; Optimisation of classifier hyperparameters; Improved statistical models for normalisation of our genome-wide methylation sequencing data and for detection of differentially methylated regions.

The optimised classifier will then be validated in a large cohort of cfDNA samples from patients with known cancer types. This is an exciting opportunity to develop and apply computational methods to high-throughput data in a translational research project with a clear line of sight to clinical application. 

*(1)Conway, Pearce, Clipson et al. Nature Communications (2024); (2)Chemi, Pearce et al. Nature Cancer (2022). 

About you:

You should have a PhD in Computational Biology/Bioinformatics, Statistics, Computer Science (or related discipline), or a relevant postgraduate degree in Computational Biology/Bioinformatics, Statistics, Computer Science or related discipline plus significant relevant experience. You will have significant experience in the development and/or application of statistical/machine learning methods, particularly supervised learning approaches, and demonstrable experience in areas of bioinformatics pertaining to the analysis of high-throughput data. You will also have significant experience in writing code for robust and reproducible analysis. Experience with generative machine learning models, Bayesian models or generalised linear models for count data is desirable, as is an understanding of liquid biopsies, cancer genomics/epigenomics, and cancer biology. 

You will have excellent communication skills and the ability to converse successfully with interdisciplinary collaborators. Experience of multidisciplinary teamwork would be beneficial.

Why choose the CRUK National Biomarker Centre?

The Cancer Research UK National Biomarker Centre () is a leading and highly specialised translational research centre within The University of Manchester (), core funded by Cancer Research UK (), the largest independent cancer research organisation in the world.
Our Centre discovers, develops, validates and qualifies biomarkers in clinical studies and trials that detect cancer earlier and predict and monitor therapy responses to support optimised treatment of patients with cancer. Our advanced research programmes, agnostic to cancer type, develop biomarkers in tissue and less invasive clinical samples such as blood (liquid biopsy) with sophisticated bioinformatic and artificial intelligence solutions for multi-modal laboratory and clinical biomarker data analysis and interpretation. Our preclinical programmes focus on development of pharmacodynamic biomarker and evaluation the efficacy of novel therapeutics in patient derived models. As a bridge between discovery science and clinical research, we are highly collaborative across Manchester, nationally and internationally.

How to apply:

To apply for this position please visit our website: