Computational Postdoctoral Fellow
A postdoctoral position is now available under the leadership of Dr Ana Cvejic (Wellcome Sanger Institute) as part of the Open Targets program.
About Open Targets:
Open Targets aims to provide evidence on the biological validity of therapeutic targets and provide an initial assessment of the likely effectiveness of pharmacological intervention on these targets, using genome-scale experiments and analysis. This pioneering public-private partnership between Sanofi, Celgene, EMBL-EBI, GlaxoSmithKline, Pfizer and the Wellcome Sanger Institute, aims to provide a R&D framework that applies to all aspects of human disease, and to share its data openly with the scientific community.
About the Project/Role:
In this project, you will analyse newly generated single cell (scRNA-Seq and scATAC-Seq) as well as novel and spatial transcriptomics data sets (In Situ Sequencing and 10x visium) to understand the molecular properties of immune cells infiltrating tumour sites. The underlying data sets comprise thousands of unique molecular signals measured for tens of thousands of cells or physical locations in each tumour. You will employ methods from high dimensional and spatial statistics to chart and understand the nature of different tumour-associated immune cells, how they interact and change biological functions and how the underlying molecular changes could be exploited as therapeutic targets.
This position will be based at the University of Cambridge in Ana Cvejic's lab.
You will have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g. Mathematics, Physics, Statistics). You will be passionate about science, hardworking, and excited to learn and improve skills in an outstanding research environment. You will be part of a multidisciplinary team and will be tasked with proactively seeking and maintaining appropriate collaborations. You will also be expected to drive forward the project, working closely together with biologists, clinicians and bioinformaticians within and outside the group to accomplish scientific objectives.
- A PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g. Mathematics, Physics, Statistics), and a passion for problem solving
- Experience in a research environment with a good publication track record
- Formal and extensive training in mathematical and statistical analysis of complex data sets
- An understanding, experience and published outcomes from analysing and interpreting large datasets using at least one statistical package (e.g. R/SPLUS, SAS) and experience in programming (e.g. Perl, Python, C++, Java)
- The ability to effectively communicate with collaborators and occasionally present oral communication to large groups
- Proven technical problem solving, data analysis and generation of novel ideas
- Team player with the ability to work with others in a collegiate and collaborative environment
- Ability to effectively prioritise, multi-task and work independently
- Demonstrates inclusivity and respect for all.
- Highly organised
Please apply with your CV and a Cover Letter outlining how you will meet the criteria set out above and in the job description.
Applications will be considered on an ongoing basis and the role may close early if a successful appointment is made.
About Open Targets
Open Targets is a pioneering public-private partnership between European Bioinformatics Institute (EMBL-EBI), GlaxoSmithKline (GSK), the Wellcome Sanger Institute (Sanger), Sanofi, Pfizer and Bristol Meyers Squibb (BMS), located at the Wellcome Genome Campus in Hinxton, near Cambridge, UK.
Open Targets brings together expertise from five complementary institutions to generate evidence on the biological validity of therapeutic targets and provide an initial assessment of the likely effectiveness of pharmacological intervention on these targets, using genome-scale experiments and analysis. Open Targets aims to provide an R&D framework that applies to all aspects of human disease to improve the success rate of discovering new medicines and share data openly in the interest of accelerating drug discovery.