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Funded PhD studentship in “Signal Processing and Data Mining Tools for the Analysis of Music al Evolution”

Dear Auditory list,

I'm very pleased to announce this funded PhD position (see below) at Queen Mary, please pass the news on to anyone interested. And sorry for cross postings, of course.

This is submitted to http://www.jobs.ac.uk/ as well, so should be up there shortly.


Queen Mary, School of Electronic Engineering and Computer Science
PhD studentship in “Signal Processing and Data Mining Tools for the Analysis of Musical Evolution”

Applications are invited from all nationalities for a funded PhD Studentship starting September 2014 within the Centre for Digital Music (C4DM) in the field of music informatics research (MIR).

Research Project. The goal of the PhD is to study the emergence of musical styles, and to study empirically what causes styles to change using an evolutionary framework. The successful candidate will research and develop robust audio feature extractors and music data mining methods, and then apply them to the study of evolution in music corpora (Jazz, World, Popular, Classical).

Supervision. The candidate will be supervised by Dr Matthias Mauch (http://www.eecs.qmul.ac.uk/people/view/2932/dr-matthias-mauch) and will join a group of around 60 full-time PhD students, post-doctoral researchers and academics in the C4DM (http://c4dm.eecs.qmul.ac.uk/). The candidate will receive further advice on the study of evolution from external advisor Prof. Armand Leroi at Imperial College.

Background. Music informatics research (MIR) encompasses research in computational methods related to music; it is an engineering discipline with an emphasis on digital signal processing and machine learning. Combining engineering methods from MIR with evolutionary biology and musicology allows us to empirically study how music changes as it is created and selected by composers and listeners. Our prize-winning paper “Evolution of Music by Public Choice” (MacCallum et al., PNAS, 2012, http://www.pnas.org/content/early/2012/06/12/1203182109) exemplifies this new cross-disciplinary approach.

Skills. Candidates should have a first class honours degree or equivalent and/or a strong MSc degree in computer science, mathematics, physics, bioinformatics, evolutionary biology or engineering. Good programming skills in either Matlab, R, Python or similar are essential, as is a passion for music. Knowledge of machine learning/data mining methods is desirable, but not essential if the candidate otherwise demonstrates good technical/mathematical skills.

Two sources of funding are available:
·     An EPSRC studentship is available to candidates with UK residency. This studentship is for 3.5 years and will cover student fees and a tax-free stipend starting at £15,720 per annum. Full details and eligibility conditions can be found at http://www.epsrc.ac.uk/skills/students/help/Pages/eligibility.aspx. Candidates should state if they are eligible for this studentship.
·     An International studentship is available to candidates without UK residency and is for 3 years.  This studentship covers student fees and a tax-free stipend of £15,720 per annum.

Please apply on-line at http://www.qmul.ac.uk/postgraduate/applyresearchdegrees/index.html by selecting “Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right hand side of the web page. 

Please note that instead of the ‘Research Proposal’ we request a ‘Statement of Research Interests’, which should answer two questions: (i) Why are you interested in the proposed area? (ii) What is your experience in the proposed area, including probabilistic model and programming? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work. This might be a chapter of your final year dissertation, or a published conference or journal paper.  More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php
The closing date for the applications is 31 January 2014.  Interviews are expected to take place during February 2014. Please contact Dr Matthias Mauch (matthias.mauch@xxxxxxxxxxxxxxx) with any queries.


Dr. Matthias Mauch

Royal Academy of Engineering Research Fellow, Lecturer.
Queen Mary, University of London.

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