We are seeking an enthusiastic, ambitious and creative computational postdoc to join the Secrier lab at the UCL Genetics Institute on a Wellcome Trust-funded position, to lead an exciting project investigating aspects of genomic instability and gene dosage during cancer progression.
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, developing a good understanding of cancer biology and particularly of cancer genomics/transcriptomics and oesophageal cancer. Applicants with a theoretical background (maths/stats, computer science, bioinformatics) are particularly encouraged to apply.
The post is funded until 29/02/2020 in the first instance.
The Secrier lab (https://secrierlab.github.io/) 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.
The successful candidate must have a PhD (or be studying towards it) in bioinformatics, computational biology, statistics, mathematics, computer science, engineering 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 bioinformatics methodologies, previous experience with NGS data and experience with cancer genomics and/or transcriptomics data are among the desirable criteria.