About the role
We are seeking a talented computational postdoc to join the Secrier lab (https://secrierlab.github.io/) at the UCL Genetics Institute, where they will take the lead on an exciting project that aims to understand how genomic instability and complex structural rearrangements in cancer cells may lead to the development of metastases. The successful applicant will employ bioinformatics, statistics, data integration and machine learning methods to investigate mutational processes that impact the transition from early cancer to progressive disease.
Duties and responsibilities of the post holder include but are not limited to: undertaking research under supervision as a member of the Secrier lab, designing, developing and refining computational/statistical methods to analyse and integrate multi-omics data in the context of cancer.
The post is initially funded until 30/04/2021.
About the lab
The Secrier lab is a multidisciplinary group working at the interface of cancer genomics and immunology. We employ bioinformatics, statistics, machine learning and data integration methodology to investigate aspects of genomic instability and tumour-microenvironment interactions for the purpose of early cancer detection and understanding of neoplastic progression. We have an established track record in oesophageal adenocarcinoma and various collaborations with partners of the UK-wide Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, including with the Universities of Cambridge, Southampton and Belfast. We also have ongoing collaborations in various other cancers, such as prostate, breast, glioma and sarcoma with clinicians and biologists at the UCL Cancer Institute, Queen Mary University and the German Centre for Neurodegenerative Diseases, among others, as well as industry partners. We are ideally based at the UCL Genetics Institute, where we benefit from a supportive and collaborative environment with a focus on method development for big genomics data.
Qualifications and skills
The successful candidate must have a PhD (or be studying towards it) in bioinformatics, computational biology, statistics, mathematics, computer science or similar area, be fluent in R, Python, Perl, C++ or other programming language, have a good knowledge of statistics and a strong interest in cancer biology.
Broad knowledge of machine learning and bioinformatics methodologies, previous experience with NGS data and experience with cancer genomics and/or transcriptomics data are among the desirable criteria.
How to apply?
For a full job description and to apply online for this vacancy please click here: http://tinyurl.com/y4djy525
Informal enquiries should be directed to Maria Secrier on email@example.com
Closing date for applications: 21st April 2019