PostDoc Fellow
Cambridge,United Kingdom
6d ago

Back to our career areas United Kingdom

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.

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.

Our Discovery Sciences team operates from truly state-of-the-art centres spanning the UK, US and Sweden. You’ll work with some of the most knowledgeable technological experts in the industry, all collaborating on high-

profile drug discovery projects

We offer an exciting opportunity to contribute to advances in the theoretical basis, and application of, representation, reasoning and inference approaches in knowledge graph and other network analysis focussed on drug discovery.

With high global interest in artificial intelligence (AI) across diverse settings, many challenges remain. In construction and analysis of knowledge graphs and other networks, there is opportunity to transfer learning from other areas of application (e.

g. social networks, epidemiology) into the drug discovery setting. Some challenges include : (1) (in)completeness of graphs, (2) bias towards positive assertions of relationships, (3) optimal embedding of graph / network topologies into forms for machine learning, with improved explainability .

This project will advance the science in these areas, with a specific focus on using machine learning for link and property prediction to aid drug target prioritisation and toxicity assessment.

Both open and AstraZeneca-specific information sources will be used.

You will be based at AstraZeneca but benefit from academic supervision by a fantastic leader in this field Prof William Hamilton at McGill University.

This project also synergises with other AI-oriented projects ongoing or starting at AstraZeneca, offering a great opportunity to collaborate, fostering creativity and driving broader impact of this research.

We’re looking for a researcher who is highly motivated, with a keen interest in this area of study, and with the skills, self-

drive and adaptability to make the most of the opportunity.

You’ll benefit from sharing knowledge and ideas with a global network of other AZ postdocs and PhD students working on machine learning and other areas, in addition to the opportunity to interact closely with full-

time researchers in a multi-disciplinary and dynamic drug discovery environment.

  • Work independently to plan and conduct research in accordance with project aims, developed in close communication with industry and academic supervisors.
  • Ensure research and results are scientifically robust, well documented and fully reproducible.
  • Extension of existing, and development of new analytical approaches to prediction and reasoning using knowledge graph / networks, focussed on drug discovery
  • Proactively engage in knowledge sharing and peer support within the AZ postdoctoral program and development of a researcher network more broadly.
  • Write-up and publish work in peer-reviewed Journals.
  • Present findings at national and international meetings, and in AstraZeneca
  • First degree and PhD in a quantitative subject (e.g. Mathematics, computer science, physics, data science, bioinformatics, machine learning / AI).
  • Research experience in machine learning.
  • Fluency in spoken and written English
  • PhD level experience in one or more of the following representation learning, graph theory, network analysis and inference, knowledge graphs and reasoning, GANs or other deep learning methods.
  • Understanding of biological concepts, data types and databases.
  • Strong mathematical knowledge, especially linear algebra
  • Programming experience and proficiency with relevant tools (e.g. Python, C++, R) and experience working in a UNIX-based distributed environment (including clusters and cloud) with software version control management (e.g. git).
  • Experience with standard machine learning libraries (e.g. SciKit Learn, PyTorch)
  • Data management, querying and manipulation skills e.g. SQL and NoSQL graph-based approaches (RDF / triplestores and labelled property graphs)
  • Understanding of network theory concepts, and use of relevant software and algorithms
  • Collaborative and self-starting attitude
  • Excellent communication and presentation skills, including experience in communicating across discipline boundaries
  • 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 Cambridge, UK with a competitive salary on offer.

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

    Advert opening date 17th June 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.

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