Research Assistant/Associate in Machine Learning for Generative Modelling in Structural Health [...]

A position exists for a Research Assistant/Associate in the Department of Engineering, to work on the development of generative modelling based approaches to population based structural health monitoring.

The post holder will be located in West Cambridge, Cambridgeshire, UK.

Responsibilities:

1. Development of approaches to the analysis of data from populations of assets.
2. Implementation of complex machine learning models into the developed population based approaches.
3. Contribution to the supervision of a group of PhD students working on related problems.

Required Skills and Qualifications:

• Knowledge of generative modelling machine learning approaches suitable for population based structural health monitoring.
• Knowledge of the implementation of complex statistical methods.
• Good writing and presentation skills.
• Good verbal and non-verbal communication skills.
• Good time management skills.
• A strong work ethic and a positive attitude are essential.
• Proof of authoring high quality academic publications.

Applicants must have (or be close to obtaining) a PhD in Machine Learning for Generative Modelling or have equivalent experience. 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.

Research Assistant: £32,296 - £34,866 Research Associate: £36,924 - £45,163

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

To apply, please ensure that you upload your Curriculum Vitae (CV), a covering letter, a publication list, and a one-page summary of what you will bring to the role 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 of a technical nature please contact Professor Mark Girolami (mag92@cam.ac.uk) and for queries related to the role or application process, contact Alexandra Roche (div-d-coordinator@eng.cam.ac.uk).

Please quote reference NM44674 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.