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Gothenburg, Västra Götaland County, Sweden
Cambridge, England, United Kingdom
Job reference: R-051890
Posted date: Jun. 19, 2019

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.

To date machine learning (ML) approaches in toxicology have also used chemical properties or assay readouts to predict classifications or effects, e.g. hepatotoxicity, that have been predetermined for each compound in the analysis. Given the significant impact of unacceptable toxicity on candidate drug progression and the potential for this to be determined only late in the discovery / development process there is a significant opportunity to enhance predictive toxicology capabilities. To date no published work has used the millions of observations made within a preclinical toxicology study as descriptors in the development of a predictive model. Furthermore, it is anticipated that greater benefit can be gained from using all measurements made at an animal level in toxicology studies and combine these to create concentration or time dependent response relationships to create predictive models. Similarly, extending these models to find associations with clinical outcomes or working backwards to look for in vitro signals in high content data sets would further enhance our predictive capabilities.

In this post-doc position, you'll be working with the latest technologies for machine learning and artificial intelligence to aid in the detection and understanding of safety risks by utilizing the preclinical data from previous drug projects to guide the development of new medicines.  As an additional benefit, this work has the opportunity to strength our commitment to the 3R’s by refining, reducing and replacing animal experiments.

Education and Experiencerequired:


  • PhD in Computational Biology or Computer Science or a related field (computational physics, mathematics, computational chemistry) preferably with a focus on bioinformatics or machine learning
  • Excellent oral and written communication skills in English.
  • Ability to work independently and excellent team working skills.


  • Good understanding of computational biology, statistics, and machine learning.
  • Basic understanding of pharmacology, toxicology or pathology.
  • Strong publication record
  • Good knowledge and hands-on experience in Deep Learning would be advantageous.

Skills and capabilities required:

  • Extensive experience with Unix/Linux OS.
  • Good knowledge of programming languages such as Python.

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

Advert opening date – 31st May 2019 / Advert closing date – 9th August 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.

If you share our passion for scientific excellence, find out more at;

Postdoc Fellow - Deep Learning in Toxicity Prediction

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