Analytical Scientist in Computational Genomics

Salary: 39,805 to 49,023 per annum inclusive dependent on skills and experience.Reporting to: Dr Syed HaiderDuration of Contract: Fixed Term for 3 years in the first instanceHours per week: 35 hours per week (Full Time)Location: ChelseaClosing Date: 13 July 2025 This role is eligible for ICR Sponsorship. Support will be provided for costs associated with Visa application. If you are considering relocating to the UK, further information can be found here.Job DetailsUnder the guidance of Dr Syed Haider, we are seeking to recruit a highly motivated researcher to apply and develop computational approaches for investigating genomic and transcriptomic determinants of heterogeneity in treatment resistant breast cancers. The successful candidate will employ computational approaches to meaningfully integrate bulk/single cell genomic/transcriptomic and imaging assays to identify mechanisms of treatment resistance in breast cancer. In addition to computational discovery; resulting suitable therapeutic targets will be subject to pre-clinical investigation and subsequent design of translational studies at our research centre. Hence, this study will be performed closely in collaboration with experimental and clinical investigators at the Breast Cancer Now Research Centre, Institute of Cancer Research, London. About youThe successful candidate must have:PhD in quantitative subject with exposure to genomic/transcriptomics datasetProgramming and scripting experienceelevant experience in statistics and geneticsDepartment/Directorate InformationThe Breast Cancer Research Data Science Team is an interdisciplinary group of researchers (~12) who are experts in high-throughput data analyses, machine learning and software engineering. We work in a highly dynamic and collaborative environment focussing on the identification of molecular markers of breast cancer by interrogating genomic, epigenomic and transcriptomic datasets profiled using bulk as well as single-cell assays. These molecular datasets are generated using patient samples and patient-derived models (xenografts and organoids), and interpreted alongside clinical covariates of patients. In particular, we are interested in the application and development of bioinformatics methods to help understand the molecular basis of treatment resistant breast cancers. The post holder will work in close collaboration with the Cancer Stem Cells laboratory led by Prof. Axel Behrens.