We are advertising for a fully-funded PhD position, to develop novel algorithmic tools for music analysis using deep learning and structured/symbolic methods. It will combine approaches from computational musicology, image analysis, and natural language processing to advance the state of the art in the field.
Music analysis is a highly challenging task for which artificial intelligence (AI) and machine learning (ML) is lagging far behind the capabilities of human experts. Solving it requires a combination of two different model types: (1) neural networks and deep learning techniques to extract features from the input data and (2) structured graphical models and artificial grammars to represent the complex dependencies in a musical piece.
The central goal of the project is to leverage the synergies from combining these techniques to build models that achieve human-expert level performance in analysing the structure of a musical piece.
This studentship is fully funded under the EPSRC Doctoral Training Partnership scheme and is hosted at the Department of Computer Science at Durham University. There will be opportunities for collaboration with researchers in other departments, including the Department of Music.
We require
- enthusiasm for interdisciplinary research in artificial intelligence and music
- an open mind-set and creative problem-solving skills
- a solution-oriented can-do mentality
- a desire to understand the structure of music and its inner workings
- a good command of a modern programming language (preferably Python) and familiarity with a modern deep learning framework (e.g. PyTorch)
- a strong master degree (or equivalent) with a significant mathematical or computational component
Please send an email with your CV and a short informal motivation to Dr Robert Lieck
robert.lieck@xxxxxxxxxxxx for initial discussions. We are looking to fill this position as soon as possible (the position is still open as long as it is advertised). The preferred start date is October 2022 (new academic year).
Dr Robert Lieck
Dr Eamonn Bell
Department of Computer Science
Durham University