Research Associate in Machine learning for longitudinal population studies with high-dimensional molecular measurements


Are you interested in working for a world top 100 university? We have an exciting opportunity in the Department of Computer Science for someone with a passion for machine learning, looking to use their skills in developing novel models for experimental design, analysis and prediction to make an impact on longitudinal population studies with high-dimensional -omics data.

You will join the Machine Learning group in our large and diverse academic department. You will further strengthen our strong international research profile and our reputation for novel, research-led teaching and work in a well-connected team with world-leading reputations in probabilistic modelling, Gaussian processes and open source software.

You will have demonstrable knowledge of a wide range of machine learning techniques, in particular, probabilistic modelling and practical experience handling data from longitudinal and/or high-dimensional studies. You will hold a PhD in Computer Science or a related area (or have equivalent experience) with a solid background in mathematics/statistics, excellent scientific programming skills and eagerness to contribute to open source software.

If you are passionate about the practical impact of machine learning research, then we would love to hear from you.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.


PhD in any quantitative discipline (eg. computer science, physics, statistics).

Start date

October 01, 2020

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