Research Assistant / Associate in Differential Geometric Approaches to Machine Learning

University of Cambridge

United Kingdom

Research Assistant / Associate in Differential Geometric Approaches to Machine Learning

This post presents an opportunity for a research scientist to join a research team based at Cambridge to work on Semantic Information Pursuit for Multimodal Data Analysis. This is part of a joint USA and UK research initiative comprised of Stanford, Berkeley, John Hopkins, Caltech, USC universities in the USA and Oxford, Cambridge, UCL and Surrey Universities in the UK -

The post holder will assist with the development of Advanced Machine Learning Theory and methodology focussed on Hamiltonian Monte Carlo and its Differential Geometric foundation with the aim of understanding the Information Pursuit in Multimodal Data.

The successful candidate will have demonstrable experience in: data modelling and analysis, experience in developing theoretical analysis of statistical methodology, experience in non-likelihood based statistical inference such as discrepancy methods, experience authoring technical documents and scientific papers, proven ability to work in a team, proven ability to communicate effectively.

Appointment at Research Associate level is dependent on having a PhD (or equivalent experience in industry). Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

Salary Range: Research Assistant: £26,715 - £30,942 Research Associate: £32,816 - £40,322

Fixed-term: The funds for this post are available for 12 months in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV) and a covering letter in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy or the application process, please contact Sue Stocks ( For queries of a technical nature related to the role, contact Prof. Mark Girolami (

Please quote reference NM22770 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

In your application, please refer to


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