Research Associate in Machine Learning-Based Spatial Audio
We have a research associate (postdoc) position to work on spatial audio processing and spatial hearing using methods from machine learning. The aim of the project is to design a method for interactively fitting individualised filters for spatial audio (HRTFs) to users in real-time based on their interactions with a VR/AR environment. We will use meta-learning algorithms to minimise the time required to individualise the filters, using...
Job listing information
- Reference ENG01810
- Date posted 20 July 2021
- Closing date 20 September 2021
We have a research associate (postdoc) position to work on spatial audio processing and spatial hearing using methods from machine learning. The aim of the project is to design a method for interactively fitting individualised filters for spatial audio (HRTFs) to users in real-time based on their interactions with a VR/AR environment. We will use meta-learning algorithms to minimise the time required to individualise the filters, using simulated and real interactions with large databases of synthetic and measured filters. The project has potential to become a very widely used tool in academia and industry, as existing methods for recording individualised filters are often expensive, slow, and not widely available for consumers.
The role is initially available for up to 18 months, ideally starting on or soon after 1st January 2022 (although there is flexibility). The role is based in the Neural Reckoning group led by Dan Goodman in the Electrical and Electronic Engineering Department of Imperial College. You will work with other groups at Imperial, as well as with a wider consortium of universities and companies in the SONICOM project (€5.7m EU grant), led by Lorenzo Picinali at Imperial.
Duties and responsibilities
- Design and test meta-learning algorithms for spatial hearing in simulations and with human participants.
- Design simulated environments and players using models of human binaural hearing.
- Design a VR/AR environment/game and test it on human participants.
You will be supported by other teams in the consortium on virtual reality, acoustics, psychophysics and modelling of the binaural system. You will:
- Liaise regularly with these teams to use the results of their work and make your results available to them.
- Present your findings both internally at project meetings and at conferences and workshops, and submit publications to refereed journals.
You will also have the opportunity to assist in supervising undergraduate and graduate research projects, as well as teaching.
We are looking for applicants with a PhD in spatial audio, audio technologies, acoustics or machine learning, or a related discipline. Ideally, you will have one or more of the following:
- Experience of applying methods from machine learning (ideally in meta-learning algorithms, although this is not essential).
- Experience with spatial audio (for example, HRTFs, models of the binaural auditory system).
In addition, you will have:
- Published high quality papers in machine learning or spatial audio/hearing.
- Excellent programming skills, especially in Python for machine learning
- Excellent verbal and written communication skills.
- Willingness to work as part of a team and to be open-minded and cooperative both internally and with external project partners.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £36,045 - £39,183 per annum.
For informal enquiries about the post please contact Dan Goodman at firstname.lastname@example.org.
Our preferred method of application is online via our website by clicking ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/ and search using reference number XXX.
Queries regarding the application process should go to Joan O’Brien at email@example.com.
Further information about the post is available in the job description.
Interviews will take place in the weeks shortly following the closing date of this advert.
About Imperial College London
Imperial College London is the UK’s only university focussed entirely on science, engineering, medicine and business and we are consistently rated in the top 10 universities in the world.
You will find our main London campus in South Kensington, with our hospital campuses located nearby in West and North London. We also have Silwood Park in Berkshire and state-of-the-art facilities in development at our major new campus in White City.
We work in a multidisciplinary and diverse community for education, research, translation and commercialisation, harnessing science and innovation to tackle the big global challenges our complex world faces.
It’s our mission to achieve enduring excellence in all that we do for the benefit of society – and we are looking for the most talented people to help us get there.
Please note that job descriptions cannot be exhaustive and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.
All Imperial employees are expected to follow the 7 principles of Imperial Expectations:
- Champion a positive approach to change and opportunity
- Communicate regularly and effectively within, and across, teams
- Consider the thoughts and expectations of others
- Deliver positive outcomes
- Encourage inclusive participation and eliminate discrimination
- Develop and grow skills and expertise
- Work in a planned and managed way
In addition to the above, employees are required to observe and comply with all College policies and regulations.
We are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender identity, sex, or sexual orientation. We are an Athena SWAN Silver Award winner, a Disability Confident Leader and a Stonewall Diversity Champion.
For technical issues when applying online please email firstname.lastname@example.org.
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