|Location:||Cambridge, Gothenburg - Sweden|
|Placed On:||23rd October 2018|
|Closes:||22nd November 2018|
We’re looking for a talented machine learning expert to join our innovative academic-style Postdoc. From our centre in Gothenburg or Cambridge, you’ll be in a global pharmaceutical environment contributing to live projects from the start. You’ll have a comprehensive training programme including a focus on drug discovery and development, given access to our existing research, and encouraged to pursue your own research in state-of-the-art laboratories.
You’ll be supported by a leading academic advisor who’ll provide guidance and knowledge to help develop your career. An exciting area that hasn’t been explored to its potential makes this an opportunity to make a real difference to the future of medical science.
AstraZeneca is a global biopharmaceutical business focussing on the discovery, development and commercialisation of prescription medicines for the world’s most serious diseases. We’re proud to have a unique culture that inspires innovation and collaboration; employees are empowered to express diverse perspectives and are made to feel valued, energised and rewarded.
One of the greatest challenges in drug development is mapping pharmacokinetic and toxicological properties, predicting how new chemicals will react within the human body. We ensure our products are safe for clinical trials and ultimately safe for patients, so you’ll be working with cutting-edge in-silico, in-vivo, cellular and molecular technologies to push the boundaries of predictive science. You’ll work in an industry leading AI group with a strong publication record.
This is a 3-year programme. 2 years will be FTC with a 1-year merit-based extension. You will be based in Gothenburg with opportunity of secondment in Cambridge, UK.
AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants 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 or any other characteristic protected by law.
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