Research Associate in Statistics/Statistical Genetics
Research Associate - £32,236 - £39,609
We are currently seeking a talented and motivated postdoctoral statistician to work on a joint project between the Cardiovascular Epidemiology Unit (Department of Public Health & Primary Care, University of Cambridge) and the MRC Biostatistics Unit. The post-holder will apply and develop cutting-edge Bayesian statistical methods for disentangling (“fine-mapping”) the plethora of genetic association signals for blood-based biomarkers, such as proteins and metabolites, and diseases (eg, heart attack, stroke). Results from these methods will help to identify novel causes of cardiovascular diseases and potential targets for drug development.
The position is underpinned by a Cardiovascular Data Science Award funded jointly by the Alan Turing Institute (www.turing.ac.uk) and British Heart Foundation (www.bhf.org.uk). This recently-established award aims to foster collaboration between cardiovascular investigators and data scientists to generate novel data science solutions to key problems in cardiovascular science. This project aims to develop a new class of scalable statistical tools for fine-mapping multi-trait genetic association data. This will facilitate the identification of “shared” and “distinct” causal variants across multiple layers of phenotypic information (e.g., gene expression, cellular traits, proteins, metabolites, lipids and lipoproteins, known risk factors, disease outcomes). Given the focus on cardiovascular science, the initial test examples will be drawn from the ~200 known genetic association signals for heart disease or stroke. Data for testing and employing these new methods will be derived from the rich resources at the Cardiovascular Epidemiology Unit (https://www.phpc.cam.ac.uk/ceu/), including the INTERVAL study, a 50,000-person bioresource with imputed genome-wide array data on all participants, >100 haematological traits, >4,000 plasma proteins, ~>1,000 metabolites, >500 lipids and lipoproteins in subsets of participants. These data have recently been harnessed to discover ~2000 genetic signals for protein levels (Sun et al., Nature, 2018) and ~6000 genetic signals for blood cell traits (Astle et al., Cell, 2016).
A PhD in Statistics, Biostatistics or Computer Science (or a closely aligned discipline), or an equivalent level of professional qualifications is essential. Postdoctoral experience is desirable. Applicants should also have a strong background in statistical modelling and computational statistics and able to implement algorithms in a low-level language (C, C++). Knowledge of approximate methods for Bayesian inference of large data sets such as Variational Bayes is also desirable. In addition to these skills, the post-holder should also be able to work independently judging priorities and have excellent organisational and communication skills.
The funds for this post are available for 2 years from commencement in post.
The post-holder will work under the supervision of Dr Adam Butterworth (Department of Public Health & Primary Care, University of Cambridge) and Dr Leonardo Bottolo (MRC Biostatistics Unit and The Alan Turing Institute, London). The post-holder will therefore be expected to spend time at both the Department of Public Health & Primary Care, located in Strangeways Research Laboratory (CB1 8RN, approx. 2 miles south of city centre), and the MRC Biostatistics Unit, located on the Cambridge Biomedical Campus and a 5 minute walk from Strangeways.
For an informal discussion about this post, please contact Dr Adam Butterworth ([log in to unmask]) or Dr Leonardo Bottolo ([log in to unmask]).
Closing date: 16th January 2019
Interview Date: Week commencing 21st January 2019
To apply online for this vacancy, please click on the 'Apply' button here: http://www.jobs.cam.ac.uk/job/19919/
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