Postdoctoral Long Title: Machine Learning applied to AstraZeneca’s Cardiovascular Outcome Trials to Predict Individual Cardiovascular & Renal Outcomes

We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Gaithersburg, US, you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training programme, including a focus on drug discovery and development, given access to our existing Postdoctoral research, and inspired to pursue your own independent research in state of the art laboratories. It’s a newly expanding programme spanning a range of therapeutic areas across a wide range of disciplines.

What’s more, you’ll have the support of a leading academic advisor, who’ll provide you with the guidance and knowledge you’ll need to develop your career. This is an exciting area that hasn’t been explored to its full potential, making this an opportunity to make a real difference to the future of medical science.

About AstraZeneca

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. But we’re more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we’re proud to have a unique workplace culture that inspires innovation and collaboration. Here, employees are empowered to express diverse perspectives – and are made to feel valued, energised and rewarded for their ideas and creativity.

As a world leader in cardiovascular, renal and metabolic medicines, we’re always working on innovative new drugs that will change lives across the globe. To make that happen, we employ a small molecule and new modalities research and development strategy with three priorities: heart failure, diabetes and chronic kidney disease. You’ll focus your efforts on making real progress toward slowing, stopping or curing some of the most aggressive diseases that face humanity today.

We’ll use artificial intelligence tools and computational modelling to predict individual long-term risk of major CV events (MACE) and renal disease based on patient characteristics and short-term biomarker changes in a very large combined database of AstraZeneca’s cardiovascular outcomes trials (patient-level data for ~180 000 individuals). This will facilitate treatment selection for patients with diabetes and cardiovascular disease by identifying characteristics that predict therapeutic response or non-response to different drug classes, inform patient selection and outcome definitions for future CV and renal outcomes trials, and characterize disease progression over time

Education and Experience requirements


  • A recent PhD and/or a recent Postdoctoral fellowship in a quantitative discipline (engineering, pharmacology, computational biology, applied math, or related) with productivity demonstrated by scientific publications and conference presentations.
  • Interest in applying quantitative tools to solve clinically-meaningful problems


  • Working knowledge of biology and physiology; experience working with clinical data a plus

Skills and Capabilities required:

  • Proficiency and hands-on experience with data analysis, machine learning, and/or computational modelling in R or similar software
  • Ability to communicate technical material to a broad audience

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 Gaithersburg, US with a competitive salary on offer. To apply for this position, please click the apply link below.

Advert opening date – 11th October 2018 / Advert closing date – 22nd November 2018

AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, gender or gender 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.

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