We are seeking a talented biomedical data scientist to join our team as a postdoctoral researcher in the Cardiovascular Epidemiology Unit (CEU) and Cambridge Baker Systems Genomics Initiative (CBSGI) of the Department of Public Health and Primary Care (DPHPC). The post will suit researchers interested in using artificial intelligence (AI) to create predictive models of biological processes. The ideal postholder will also have an interest or experience in analysing genomic, multi-omic and phenotypic/health-record data at population scales.
The primary role of the post holder will be to lead projects that develop new AI models, benchmark existing models and combine multiple approaches to predict biochemically important parameters. A challenge will be developing models and tools which can be applied rapidly and at scale in human populations (e.g. >1 million individuals). The post holder will also be deeply involved in the quantitative analysis and interpretation of genomic, multi-omic and phenotypic data using polygenic scores, GWAS, Mendelian randomisation, and other statistical and machine-learning methods.
The appointed researcher will have research interests aligned with those of our groups and the freedom to develop their own ideas for new research by contributing to the preparation of new grant applications. Initial possibilities for research projects include:
- Leveraging biochemical knowledge and databases (e.g. REACTOME) to model and interpret multi-omics data and complex phenotypes
- Integrating multi-omics data to dissect the molecular etiology of cardiometabolic diseases
- Combining genetics and structural bioinformatics to understand variant-to-function and potentially improve therapeutic outcomes
The post-holder will be expected to evaluate and develop the statistical methods necessary to test hypotheses of interest, such as those listed above and advise on appropriate statistical practices. However, we are also open to exploring other research questions and proposals from candidates that could be addressed using datasets available to us locally or through collaboration. The work of the post-holder is expected to lead to first author high-impact publications.
The preferred candidate will have a PhD in a subject related to data science: artificial intelligence, machine learning, computer science, statistics, biostatistics, statistical genetics, computational biology, bioinformatics, epidemiology or similar. They will have in-depth knowledge of and demonstrated experience of current models for deep learning and machine learning, and strong quantitative (in silico) analysis skills and experience using statistical programming packages (e.g. R) and/or scripting languages (e.g. Python), and experience working on computing clusters/computers running Linux based operating systems.
This position is available full-time or part time (0.8FTE) immediately until 30 September 2028.
We strongly value and encourage Equity, Diversity and Inclusion as well as a flexible working environment.
Location of post: Victor Phillip Dahdaleh Heart & Lung Research Institute, Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, next to the Cambridge South train station.
If you have any questions about this vacancy or the application process, please contact Professor Michael Inouye
To apply online for this vacancy and to view further information about the role, please click 'Apply' above.
Appointment at Research Associate level is dependent on having a PhD (or equivalent experience), including those who have submitted but not yet received their PhD. Where a PhD has yet to be, awarded appointment will initially be made at research assistant and amended to research associate when the PhD is awarded.
The University actively supports equality, diversity and inclusion & encourages applications from all sections of society.
:quality(80))
:quality(80))
:quality(80))
