PhD in Data-driven quality prediction for digitally restored audio archives


The project is an exciting opportunity to use cutting-edge data-driven techniques to build machine learning-based prediction models. The candidate will explore how to predict the fidelity of archive music recordings – understanding, detecting, quantifying and classifying noise and the effects of digital restoration techniques. The project will also involve developing an understanding Quality of Experience – i.e. how we form quality judgments and how we perceive sound. 

In collaboration with the Alan Turing Institute and Queen Mary University of London

We invite applications for a PhD fellowship at the INSIGHT Centre for Data Analytics, University College Dublin. The position is funded by Science Foundation Ireland. The research project will be carried forward in collaboration with the Alan Turing Institute (ATI) as a strategic research partner, and Queen Mary University of London. In order to facilitate this, the successful PhD candidate will be seconded and sponsored to spend up to 25% of the time at ATI in London.

This is a 4 year fully funded PhD position, with a PhD scholarship of euro 18,500 per year (no tax, PhD fees are paid) and a substantial financial support for training and exchange visits to ATI, as well as sponsorship for conference travels.

Candidates must be highly motivated with the ability for independent and critical thought. Candidates must have a first-class honours degree or equivalent, and/or a good MSc Degree in Computer Science, Electronic Engineering, Music/Audio Technology, or a related discipline. Knowledge of machine learning, data science, digital signal processing and/or cognitive science is desirable, as well as programming experience in, e.g. Python, Matlab, C++ or similar. Experience with perceptual evaluations and user studies is desirable. Experience in research and a track record of publications is advantageous. There is scope to tailor the research to the interests and skills of the successful candidate.

Essential Skills: 

Other skills and traits of an ideal candidate: intellectual curiosity; the ability to organise and prioritise their own work and organise research within the PhD timetable; 


Effective team working; excellent communication and writing skills in English; analytical skills; understanding of the research process; flexible and co-operative; self-motivated and hardworking; willingness to learn new skills; interest in music and proficiency in an instrument.


Application process:

Interested applicants are requested to submit (in pdf format):

-       a Curriculum Vitae including relevant publications, experience and the name and contact details of at least 2 referees

-       a one page cover letter explaining your motivations and why you think you would be the best candidate for the position.

Submissions should be sent by email with subject “SFI-AudioArchivePHD Application” to by Sunday 15th July 2018.

Interviews will be carried out as suitable candidates are identified.

The successful candidate is expected to start in September 2018.

Application End Date:

Sunday 15th July 2018


Informal queries about this position should be sent to