Postdoc – Machine Learning Modelling

Postdoc Fellow- Bayesian Machine Learning Modelling with applications in Clinical Trial Predictions

Central Cambridge, UK or Gaithersburg, US

Competitive salary on offer, excellent flexible benefits package, annual bonus

Considering a Data Science Postdoc in industry? Look no further…

About our Postdoc Programme:

We’re currently looking for dedicated scientists and researchers to join our innovative academic-style Postdoc Programme. From our centre in Cambridge, UK or Gaithersburg, US, you’ll be in a global pharmaceutical environment, contributing to live projects right from the beginning. You’ll take part in a comprehensive training programme, with access to cutting-edge Machine Learning and AI ideas, given access to our existing Postdoctoral research, and encouraged to pursue your own independent research.

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 a chance to make a real difference to the future of medical science.

About this Opportunity:

In this role, you will join our Data Science & Artificial Intelligence (DS&AI) group which has direct strategic impact on drug development, playing a key role in delivering medicines to patients. Here, you’ll help accelerate the potential of medicines and science, and have the opportunity to make a positive impact on patients’ lives every single day.

As a Postdoc Fellow, you will be developing research ideas under the supervision of a Data Science Director and an external academic mentor. Applying your knowledge and pushing it further. It’s not always easy, but it’s always rewarding. A place full of challenges and opportunity where you can be adventurous and courageous to push boundaries, change the game, innovate and make an impact.

You will be working on a project to produce novel statistical methods in Bayesian spatial-temporal models for prediction or recruitment and retention for clinical trials. This rewarding role will develop you as a researcher and place you in a supportive environment for bringing forward your ideas. We are looking for a motivated researcher seeking to enrich their experience and build their research profile to international excellence.

If that sounds like you, read on!

Main Responsibilities:

  • Conduct and plan scientific work with appropriate supervision
  • Develop novel machine learning approaches
  • Maintain highly organised and accurate record of experimental work and complete records of all findings
  • Publish in high quality journals and present at national and international meetings
  • Actively participate in the research programme of the group, research meetings and internal seminars.

Required Qualifications, Skills and Experience:

  • PhD in Statistics/Mathematics/Computer Science or related subject
  • Excellent knowledge of advanced statistical and machine learning methods
  • Recent track record of publications in leading international journals and conferences in an area relevant to statistics, Bayesian methods and/or machine learning
  • Excellent verbal and written communication skills.
  • The ability to work collaboratively with a diverse group of people
  • Ability to prioritise own work
  • Strong experience in R programming

Desirable Qualifications, Skills and Experience:

  • Experience of communicating sophisticated statistical and mathematical concepts to a variety of audiences
  • Experience of oral presentations at international conferences
  • Experience in using version control systems (GIT, SVN) and agile software development process
  • Working knowledge of clinical trial methodology and design

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, or Gaithersburg, USA, with a competitive salary on offer

Advert opening date – June 11, 2021

Advert closing date – July 11, 2021

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