Wellcome Genome Campus, Hinxton
Financial incentives: Monthly family, child and non-resident allowances, annual salary review, pension scheme, death benefit, long-term care, accident-at-work and unemployment insurances
Flexible working arrangements
Private medical insurance for you and your immediate family (including all prescriptions and generous dental & optical cover)
Generous time off: 30 days annual leave per year, in addition to eight bank holidays
Relocation package including installation grant (if required)
Campus life: Free shuttle bus to and from work, on-site library, subsidised on-site gym and cafeteria, casual dress code, extensive sports and social club activities (on campus and remotely)
Family benefits: On-site nursery, 10 days of child sick leave, generous parental leave, holiday clubs on campus and monthly family and child allowances
Benefits for non-UK residents: Visa exemption, education grant for private schooling, financial support to travel back to your home country every second year and a monthly non-resident allowance.
The Cortes-Ciriano group co-leads the CRUK-funded Stratified Medicine Pediatrics Programme (SMPEDS) and serves as the data coordination centre for the study. SMPEDS is the UK national molecular profiling platform for children with relapsed cancer and it successfully provides infrastructure to triage patients into clinical trials by identifying molecular abnormalities in rapid turnaround, with clinical reporting through a national weekly National Molecular Tumour Board. The main objective of SMPEDS is to facilitate better outcomes for children with relapsed cancers by identifying novel and potentially actionable events, treatment resistance factors and implementing minimally invasive diagnosis and disease monitoring via the use of liquid biopsies. In addition, SMPEDS ensures that research multi-omics is routinely performed on UK patients who relapse with cancer. Technologies used by SMPEDS investigators include spatial and single-cell tissue profiling, digital pathology and machine learning, and whole genome scale liquid biopsy and methylation.
More broadly, the group develops computational tools to realise the vision that one day early cancer detection will be a routine activity using minimally invasive techniques, and to characterise the patterns of mutations and genome instability processes in human cancers. The group uses diverse data analysis techniques and experimental methods, with a strong focus on the analysis of genome sequencing data from clinical samples.
We are looking for an intrinsically motivated and talented individual with experience in managing, processing, and analysing high-throughput sequencing data sets. This is primarily a technical role that represents an excellent opportunity for individuals looking to gain in-depth expertise in the coordination and management of multi-omic and clinical data to support a large UK-wide clinical study. You will be expected to lead the development and implementation of scalable computational solutions for the transfer, processing and analysis of sequencing data sets generated by the SMPEDS programme. These data sets will include a unique collection of hundreds of long-read whole-genome sequencing data sets spanning multiple paediatric cancer types, as well as spatial genomics and liquid biopsy data sets. It is essential that you are dedicated to lead your own work while also being a team player and willing to engage with our national and international collaborators. Your work will involve close interactions with SMPEDS investigators and other EU initiatives with which the program collaborates.
PhD degree in the life sciences (preferably computational biology, genomics, statistics, or a similar field) or an MSc degree with equivalent years of experience working in data management and bioinformatics
Strong analytical and programming skills (preferably in python)
Proficient communication skills in both written and spoken English
Experience in Unix-based environments, high-performance computing and the development of reproducible data analysis pipelines using version control software •
Experience in database technologies
Experience in computing using software containers and workflow management software, such as Nextflow and Docker
Experience in large-scale data transfers using state-of-the-art tools, such as Aspera or Globus
Significant experience in computational genomics (ideally in cancer genomics) as evidenced by publication record
Demonstrated ability to work both independently and collaboratively with other group members and external collaborators
How to Apply
How to apply: To apply please submit a cover letter and a CV through our online system.
Closing date: 23:00 GMT on 18 March 2024
About Our Organization
EMBL-EBI is international, innovative and interdisciplinary, and a champion of open data in the life sciences.
We are part of the European Molecular Biology Laboratory (EMBL), an intergovernmental research organisation funded by over 20 member states, prospect and associate member states.
We are situated on the Wellcome Genome Campus near Cambridge, UK, one of the world’s largest concentrations of scientific and technical expertise in genomics.