AddressThe Role
This is an exciting opportunity for a methodologically-focussed researcher with strong computational/statistical skills to contribute to cutting-edge methods development in a clinically relevant research area with strong translational potential.
Your role will be to develop and apply advanced statistical analysis methods to multi-omics data (SNP genotypes, gene-expression, CpG methylation and serum proteomics) derived from patients with liver disease and healthy controls, to identify causal pathways in disease development/progression.
The project forms part of a Wellcome Investigator Award in Science awarded to Professor Heather Cordell. The overall goal of this research is to develop and apply advanced statistical methods to help elucidate the biological mechanisms and causal pathways underpinning the correlations seen between genotype and phenotype in complex genetic disorders, with specific emphasis on liver and kidney disease. This will allow us to better understand the biological processes leading to disease development, thus enabling the development of potential therapies and cures.
Identifying genes and their protein products which alter disease risk will point to potential new drug targets and allow opportunities for re-purposing of existing drugs. We will use measurements of genetic factors and potential intermediate processes such as gene expression, DNA methylation and protein levels, available through long-standing collaborations with clinical colleagues. A key goal of our research is to develop methods that integrate these different data types with one another, and with similar data from external sources.
Key Accountabilities
- Apply existing statistical methodology and, where required, develop new methodology to carry out the specified project using appropriate techniques as outlined in the person requirements;
- Working under the general guidance of the Principal Investigator, contribute your own ideas, including enhancements to the technical or methodological aspects of their studies, thus providing substantial 'added value';
- Contribute to the writing up of their research for publication and dissemination, either through seminar and conference presentations or through publications;
- Develop user-friendly software implementations of any novel analytical methods generated;
- Contribute to the deposit of software and study data to appropriate repositories;
- Contribute to research and teaching activities being carried out in the wider group, including providing informal advice to more junior researchers;
- May be involved in the supervision, with guidance, of final year undergraduate research projects and in providing support to postgraduate research students or Research Assistants;
- May contribute to events celebrating the public engagement of science/mathematics/statistics/computational biology;
- Develop an awareness of University structures, policies and procedures and relevant issues in the higher education, research, social and political environment.
The Person
Knowledge, Skills and Experience
- Hard working and enthusiastic;
- Detailed subject knowledge in the area of applied statistics;
- High level of analytical and problem-solving capability;
- Ability to work well as part of a team and rapidly acquire new skills;
- Ability to communicate complex information with clarity;
- Experience of carrying out research with clear transferable skills and some experience or awareness of the research environment.
Desirable
- Detailed subject knowledge of existing statistical/computational methods and approaches for analysing high-dimensional data;
- Detailed subject knowledge of existing statistical/computational methods and tools for analysing human genetic data;
- Familiarity with analysing multi-omics data;
- Scripting/programming ability using a command line interface;
- Some knowledge of one or more programming languages suitable for working in the area of statistics and data science (e.g. R, Python, Julia, C/C++);
- Presentations at conferences and/or high-quality publications.
Qualifications
- Good honours degree (or equivalent) in a quantitative discipline (e.g. mathematics, physics, statistics, computer science, bioinformatics) with some subject knowledge in applied statistics, genetics or computational biology for Research Assistant;
- A PhD in an appropriate research area (e.g. statistics, Statistical Genetics, computational biology, genetic epidemiology) for Research Associate.
