The Garnett lab are seeking a Senior Bioinformatician to play a pivotal role in the development of innovative cancer drug combinations therapies.
Many cancer patients have tumours which are resistant to existing treatments. Combination therapies aimed at inhibiting multiple targets can provide new treatments for patients and combat drug resistance. In this role, you will join a multidisciplinary team focused on identifying and validating targets for combination drug therapies. The role includes curating, analysing and modelling datasets to generate insights from complex and large-scale multi-dimensional functional and genomic datasets. You will work in team environment with cell biologists, biochemists, and other data scientists to deliver high value targets for drug development. This an opportunity to develop transformative medicines for cancer patients at the interface of academic research and industry.
The Translational Cancer Genomics team, led by Dr. Mathew Garnett, investigates how genetic alterations in cancer contribute to disease and impact on response to therapy. Our research integrates cancer genomics, cell biology and cancer therapeutics and employs cutting-edge experimental and data science approaches to tackle some of the biggest challenges in cancer.
The project is funded by Open Targets, a private-public partnership between academia and industry. Further details of the Garnett lab (https://www.sanger.ac.uk/science/groups/garnett-group) and Open Targets are available at the provided links.
Essential Skills
Technical Skills:
A PhD or equivalent relevant experience in computational biology, bioinformatics, biostatistics or related scientific discipline
Experience in the application of genomic datasets including DNA and RNA sequencing data
Training in statistical methods appropriate for biological research
Experience working in a research environment
An established track record of productivity
Proficiency in one or more scripting languages
Competencies and Behaviours
Excellent communication and organisational skills.
Proven independent working style, technical problem solving, data analysis and generation of novel ideas
Motivation and ambition to make a personal contribution to GRL research.
Proven ability to work effectively within a team.
Ability to work multi-task and organise own workload.
Good attention to detail and record keeping.
Demonstrates inclusivity and respect for all.
Other information
Please apply with your CV and a Cover Letter addressing how you meet the criteria set out above and in the job description.