Research Assistant / Associate in Machine Learning Systems
University of Cambridge
United Kingdom

Research Assistant: (£26,715 - £30,942) Research Associate: (£32,816 - £40,322)

Fixed-term: The funds for this post are available for 2 years.

We are seeking to appoint two Research Assistants/Associates to join a team working on the developing machine learning methods within the systematic approach we call Auto-AI. The aim of this project is to look at machine learning systems in deployment with a holistic view. How can we design safe, reliable and interpretable methods that are capable of interacting in a deployed critical system? Where machine learning traditionally have studied methods and task in isolation, this project focuses on understanding and developing these methods with a system perspective. How can we understand sequential decision processes at scale, how can we create the required software and data architectures, how can we optimise and adapt large systems when the circumstances change.

This position can be filled by an appropriate candidate at Research Assistant or Research Associate level, depending on relevant qualifications and experience. Appointment at Research Associate level is dependent on having a PhD (or equivalent experience). Where a PhD has yet to be awarded the appointment will initially be made as a Research Assistant level, which will then be amended to Research Associate when the PhD is awarded.

Essential requirements: Candidates should have a degree in Computer Science/Statistics/Mathematics or related discipline (Research Associate) or significant experience in the deployment of Machine Learning methods (Research Assistant).

Desirable skills: Experience in working with probabilistic models, in specific Bayesian methods such as Gaussian processes and latent variable models. Experience in sequential modelling and decision processes such as Bayesian optimisation or Reinforcement learning. Being that this project looks at the integration of models into a system, experience in deployment of machine learning methods is beneficial. The project involves a set of partners not located in Cambridge why experience of working in teams using revisions control, continuous integration and test driven development are advantageous.

Applicants should contact Prof. Neil D Lawrence (https://inverseprobability.com/) or Dr. Carl Henrik Ek (https://carlhenrik.com) for further information.

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

Please provide a CV and covering letter. If you upload any additional documents which haven't been requested, we will not be able to consider these as part of your application.

Please quote reference NR23535 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.

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