Research Associate in Biomedical Data Science (Fixed Term)
We are currently seeking a highly motivated and talented postdoctoral researcher in biomedical data science to join our team at the Cambridge Baker Systems Genomics Initiative (CBSGI) and Cardiovascular Epidemiology Unit (CEU) at the Department of Public Health and Primary Care (DPHPC). The post would suit naturally collaborative, self-motivated researchers interested in the aetiology and prediction of cardiovascular and metabolic diseases as well as their common comorbidities. The post holder will have experience in the application of machine learning, artificial intelligence, bioinformatics and biostatistics to analyse large-scale genomic and phenotypic data in the CEU's broad portfolio of multi-omic cohorts.
The primary role of the post holder will be to lead projects involving the quantitative analysis and interpretation of genomic and phenotypic data using polygenic scores, GWAS, Mendelian randomisation, and other statistical and machine-learning techniques. The successful role-holder will have research interests aligned with those of CBSGI and CEU. They will have the freedom to develop new research ideas, in particular by leading/contributing to new grant or fellowship applications.
Initial potential research projects include: (i) the role of iron deficiency in cardiometabolic disease risk across populations, (ii) integrating multi-omics data to determine the molecular etiology of specific cardiometabolic multimorbidity patterns, (iii) uncovering demography-based heterogeneity in causal effects of molecular traits on disease outcomes, or (iv) predicting multimorbidity trajectories by combining data from e-health records, lifestyle factors, and genetic data.
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 that could be addressed using data available to us. The work of the post-holder is expected to lead to first author high-impact publications.
This position will be supervised by Professor Michael Inouye (Professor of Systems Genomics and Population Health). CBSGI conducts interdisciplinary research with a major focus on developing and applying statistical and machine learning approaches to multi-omics data from studies of >150,000 participants, with the goal of understanding aetiology, and improving the prediction and prevention of cardiometabolic, respiratory and related diseases. Our groups have a strong commitment to open-science and have created widely used resources (www.PGSCatalog.org, www.OmicsPred.org) and software tools (https://github.com/PGScatalog/pgsc_calc). More information about our collective interests and activities can be found at http://www.phpc.cam.ac.uk/ and http://www.inouyelab.org/.
The preferred candidate will have a PhD in in a subject related to biomedical or health data science: Statistical Genetics, Computational Biology/Bioinformatics, Biostatistics, Epidemiology, Statistics, Computer Science, Mathematics or similar or equivalent experience. They will have In-depth knowledge of and demonstrated experience in statistical genetics (e.g. GWAS, QTL analysis, polygenic scores), 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.
The funding supporting this short-term post is available for up to 4 months from commencement in post.
Located at the new Victor Phillip Dahdaleh Heart & Lung Research Institute, Cambridge Biomedical Campus, Papworth Road, Trumpington, Cambridge CB2 0B
Informal enquiries are welcomed and should be directed to: Professor Mike Inouye mi336@medschl.cam.ac.uk
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Please ensure that you upload a covering letter and a CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.
The closing date for applications is: 31st October 2024
The interview date for the role is: 4th November 2024
Please quote reference RH43614 on your application and in any correspondence about this vacancy.
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