Postdoc Fellow – AI/ML derived Phenotypes from 500K Exomes
AstraZeneca is a global, innovation-driven biopharmaceutical business that focuses on the discovery, development and commercialisation of prescription medicines for some of the world’s most serious diseases. We're proud to have a unique workplace culture that inspires innovation and collaboration. We believe in the potential of our people and you’ll develop beyond what you thought possible.
The new R&D Oncology organisation brings together early and late oncology teams, from discovery through to late-stage development, with oncology specific Regulatory and Biometrics groups.
You’ll be in a global pharmaceutical environment but also exposed to strong rigorous academic science. For example, every postdoc has an external academic mentor to ensure we are working and publishing at the highest level in a field. What’s more, you’ll have the support of a leading academic advisor, who’ll provide you with the guidance and knowledge you need to develop your career.
You’ll join AZ’s Genomics Initiative, which aims to analyse data from up to two million genomes through a network of academic collaborations, including genomic samples donated by patients in its clinical trials. This genome sequence data is linked to rich phenotypic records, including data on disease, drug response data and patient outcomes. Focusing on our core key therapeutic areas (oncology, respiratory, cardiovascular, renal and metabolic disease), this initiative is expected to provide novel insights into the biology of disease, identify and validate new targets for medicines, improve selection of patients for clinical trials and match marketed treatments to patients most likely to benefit. Using the power of genomics to better define disease supports our ambition to develop the most innovative and impactful treatments for patients. Genomics is foundational to our research and development and help us in the discovery of the next wave of targeted innovative medicines. Join us and you could play a role in enabling this.
The goals of this project in AZ’s Centre for Genomics Research focusses on exploring and applying machine learning approaches to identify phenotypic sub-groups, described here as ‘smart phenotypes’, among the UK Biobank (500k individuals) and to apply robust statistical genomic methods to identify novel genotype-phenotype associations in cardiovascular, renal, metabolic, oncology and respiratory disease. Such novel associations have the potential to yield insight into disease biology.
You’ll work with the CGR Analytics & Informatics team using AZ’s advanced access to the UK Biobank, which combines genetic (genotyping and whole exome sequencing [WES]) and phenotypic data from over 500,000 individuals in an unprecedented combination of breadth and depth. The UK Biobank provides an exemplar opportunity to apply advanced analytics including WES-based PheWAS and machine learning algorithms to address the goals of the project.
You’ll be involved in defining, developing, and executing the analysis plan for this project, and interpreting results with a team of peers. You’ll also be involved in driving and writing manuscripts for publications and oral presentations of results.
Your research will be supervised by Dr Slavé Petrovski (VP & Head of Genome Analytics & Informatics) and Dr Keren Carss (Genome Analyst, AZ Centre for Genomics Research), with external academic supervision from Prof. David Balding of the University of Melbourne. You’ll have the opportunity to collaborate with world leading experts in large-scale human genetics, both within AZ and outside of the company. Your research will lead to high quality peer-reviewed publications and the opportunity to present at national and international meetings.
Education and Experience required:
• A Doctoral degree in human genetics, statistical genetics, bioinformatics, computer science, machine learning, or other relevant discipline.
• Demonstrable expertise in analysis of large-scale human genetics sequencing data.
• Experience with application of advanced analytics, including machine learning, in genomics.
• Expertise in programming, including in R, Perl or Python, and using high performance computing clusters (e.g., Slurm).
• Knowledge of genomics community algorithms and solutions. Interest in the potential of genomics to impact drug discovery.
• Proven communications skills, including writing of manuscripts and oral presentations.
• Experience querying and analysing complex phenotypic datasets and electronic health records.
• Track record of first-authored work published in reputable scientific journals.
Skills and Capabilities required:
• Highly motivated, well organized and resourceful.
• Able to work well both as part of a team and independently.
• Excellent communication skills (both oral and written).
This is a 3 year programme. 2 years will be a Fixed Term Contract, with a 1 year extension which will be merit based. The role will be based in Cambridge, UK, with a competitive salary on offer. To apply for this position, please click the apply link below.
Advert opening date – 2nd October 2019 / Advert closing date – 3rd December 2019
AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.