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Job description

Incorporating biological evidence in language models to find novel knowledge
Thesis project: 30 credits, starting in January 2021

If you are interested in natural language processing and deep learning, and would like to explore the limits of representing knowledge based on text and knowledge bases, this is the project for you! Be part of discovering novel knowledge at scale!

At AstraZeneca, we turn ideas into life changing medicines and strive to continuously meet the unmet needs of patients worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.

In R&D IT and specifically in the AI Engineering Lab, we create scalable AI capabilities for our research colleagues, with the constant aim of delivering better science, faster.


There has been extensive research on using knowledge graphs to augment language models. For instance KnowBert takes a pre-trained language model (e.g. BERT) and appends an ad-hoc mechanism that incorporates knowledge from a knowledge base to produce even more informed contextual embeddings for each token.

Biology is a very knowledge rich domain, and incorporating said knowledge into the LM would potentially lead to much better contextual representations of entities. A lot of our use-cases revolve around finding entities that should share traits with a given one, e.g. finding all other diseases related to a given disease.

This project would amount to inducing the information contained in external knowledge bases into a language model, and assess the quality of the contextual word embeddings, and compare them to their non-enriched counterparts. The hope is that we would be able to extract potentially novel knowledge in this way.


Essential Requirements
  • Knowledge of deep learning, transformers, pytorch

Desirable Requirements
  • Natural language processing
  • Having worked with knowledge bases
  • Cloud/devops experience 


Randstad Life Sciences is cooperating with AstraZeneca in this recruitment process. We only accept applications through Randstad’s website.

Deadline for application: 2020-11-05, selection and interviews will be ongoing. The position may be filled before the last day of application, therefore, apply as soon as possible.

For more information: Kerstin Karlsson [email protected] or Linnea Öster [email protected]

About the company

Our Gothenburg site is one of AstraZeneca's three strategic science centers. We thrive in a multinational environment working cross-functionally across the globe with AstraZeneca colleagues as well as academic and industry partners. Our way of life is to foster a working environment that nurtures, collaboration, openness and innovation. Therefore, we have created space for meetings, socializing and relaxation, where spontaneous meetings can give birth to new innovations. The unexpected ideas or thoughts that can come from a chat over something as simple as a cup of coffee or a stroll on our “walk and talk” meeting trail.

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 only employs individuals with the right to work in the country/ies where the role is advertised.

Detta är en jobbannons med titeln "Thesis work: Incorporating biological evidence in language models" hos företaget Randstad AB och publicerades på workey.se den 22 oktober 2020 klockan 13:15.

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