AstraZeneca Pharmaceuticals LP Post Doc Fellow - Optimizing Clinical Development Programs using Bayesian Statistic and Machine Learning Modelling in Waltham, Massachusetts

Postdoctoral Long Title:

Optimizing Clinical Development Programs using Bayesian Statistic and Machine Learning Modelling

We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Waltham, MA, 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 encouraged to pursue your own independent research in cutting edge 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 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.


This is a full-time position or a postdoctoral position in the area of statistical modelling in oncology. You will be based in the Department of Quantitative Clinical Pharmacology in AstraZeneca in Waltham, MA, and will be a member of the Interdisciplinary Program in Pharmacometrics which links statistical, pharmacometric experts across the Scientific Core Platforms.

Education and ExperienceRequired:


  • A PhD with a track record of productivity in the areas of Bayesian statistics, mathematics, pharmacometrics, engineering or related field, as manifested in publications and presentations

  • Good written and verbal communication skills as the position will require working with colleagues in cross-geographic locations

  • Good team-working skills in a multidisciplinary environment

  • The ability to share knowledge and interpret findings

  • A strong scientific track record, as evidenced by published or accepted papers in peer-reviewed journals, oral and/or poster presentations at scientific meetings


  • Mathematical or statistical modelling, with experience in modelling biological data

  • Hands-on familiarity with software platforms such as R, SAS, Stan, or other software/scripting languages

  • An understanding of pharmacology, and oncology therapeutic areas

  • The capability and drive for performing applied statistics research, without the need for close supervision

Skills and Capabilities required:

  • Develop statistical and pharmacological models in oncology, working as part of a modelling team and collecting clinical data in oncology, both from the public domain and in-house data

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 at Gothenburg, Sweden with a competitive salary on offer

To apply for this position, please click the apply link below.

Advert opening date –5th March 2018

Advert closing date – 13th May 2018

AstraZeneca welcomes applications from all sections of the community.

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.

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