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Job Description

A Postdoctoral position in computational biology/machine learning is available in the Statistical Genomics and Systems Genetics group (http://www.ebi.ac.uk/research/stegle) at the European Bioinformatics Institute (EMBL-EBI) located on the Wellcome Genome Campus near Cambridge in the UK.   Are you interested in finding innovative ways to predict gene/disease associations from high-throughput genomic data? As an EMBL-EBI postdoc working on a CTTV (Centre for Therapeutic Target Validation) project, you would collaborate with research teams in CTTV partner organisations to derive new methods to predict gene/disease targets. The project offers a unique opportunity to address a major question in human health by deriving innovative statistical models that fully exploit the cutting-edge datasets generated within the CTTV. 
This is a position with the Stegle group at EMBL-EBI, which is developing statistical data-integration methods to predict drug targets by combining information about gene/disease associations from several different sources.

The CTTV is a public-private initiative that is using high-throughput datasets to generate evidence on the validity of therapeutic target, and is committed to sharing its data openly with the scientific community.
  EMBL-EBI is part of the European Molecular Biology Laboratory (EMBL). We are a world-leading bioinformatics centre providing biological data to the scientific community with expertise in data storage, analysis and representation. We provide a dynamic, international working environment and have close ties with both the University of Cambridge and the Wellcome Trust Sanger Institute. EMBL-EBI staff enjoy many benefits including excellent sports facilities, a free shuttle bus to Cambridge and other nearby centres, an active sports and social club and an attractive working environment set in 55 acres of parkland.  

Qualifications and Experience

 * You are the kind of person who can work independently and come up with creative solutions. You can set clear goals, articulate scientific and technical requirements clearly, and collaborate in an interdisciplinary setting.

* You have experience developing computational methods and implementing them in software in a scientific context. 

* You hold a doctoral degree (or equivalent qualification) in computer science, statistics, mathematics, physics, or engineering, and/or a degree in biological science.

* You are proficient in a high-level programming language (e.g., C++, Java) and scripting languages.

* You are proficient in using statistical data analysis tools such as R, MATLAB or Python.

Ideally, you also have:

* expertise in analysing and integrating multi- 'omics data, and in statistical interpretation and analysis of next-generation sequencing datasets;

* experience in statistical aspects of matrix completion methods or Bayesian network learning, preferably in the context of biology;

* experience using methods in: statistics, machine learning, matrix factorisation, non-parametric statistics, optimisation or mathematical modelling. 

* a background in biology, or previous experience tackling biological questions is beneficial but not required.