Research Assistant/Associate in Machine Learning for Audio Modelling at University of Cambridge

Posted on Sep 14, 2020 in Job Opportunities in Acoustics

Closing date for applications: 4 October 2020

We are seeking a Postdoctoral Research Associate in the general area of machine learning for audio to join the team of a 2.5M Euro European Research Council funded project entitled “EAR: Audio-based Mobile Health Diagnostics”.

The general aims of the project are to advance the use of audio for automatic diagnostics of clinical conditions. More widely the project aims to devise robust in-the-wild audio analytics targeting clinical applications and develop new on-device machine learning paradigms.

For this specific post we are seeking candidates with background in machine learning (and signal processing) for audio analytics. The position is available for 3 years.

Requirements: Candidates should have been awarded a PhD (or be very close to submission of one) and ideally have a strong publication record in machine learning for audio or acoustics and signal processing. The candidate should also have good programming skills. Good communication, presentation and management skills are also desirable given the size of the project team.

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 and amended to research associate when the PhD is awarded.

Applicants should contact Prof Cecilia Mascolo for further information https://www.cl.cam.ac.uk/~cm542/

To apply online for this vacancy and to view further information about the role, please visit :
http://www.jobs.cam.ac.uk/job/26821.

Please ensure you upload a covering letter, a curriculum vitae, a brief research statement, two key publications and contact information of 2 references. 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 quote reference NR23958 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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