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Fully Funded PhD in Music Information Retrieval for Irish Traditional Music at Maynooth University Maynooth University in Ireland
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded for four years through a Maynooth University Doctoral Scholarship. Funding includes a student stipend of €25,000 per annum and annual tuition fee support. The award runs from February 2027 to January 2031, subject to satisfactory annual academic progression.
Deadline
Oct 19, 2026
Country
Ireland
University
Maynooth University
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About this position
Maynooth University’s Department of Computer Science is offering a fully funded PhD project on Music Information Retrieval for Irish Traditional Music. The project is based in Dublin, Ireland and begins in February 2027.
This research explores advanced MIR methods for Irish Traditional Music (ITM), combining traditional signal processing with modern deep learning. The successful candidate will work on automatic note transcription, tune-type classification (reels, jigs, hornpipes), ornamentation detection, instrument recognition, structural form analysis, and modelling stylistic variation across performers and regions.
The project is especially relevant to students interested in Computer Science, Machine Learning, Data Science, Data Analysis, Music, and audio signal processing. It also connects with digital music archives, music education tools, recommendation systems, and cultural heritage preservation.
Funding is provided through a Maynooth University Doctoral Scholarship for four years. The award includes a €25,000 annual stipend plus annual tuition fee support. Applicants must be resident in Ireland and available for full-time research at Maynooth University.
Eligibility highlights include a first-class or 2.1 honours degree, or a relevant Master’s degree in Computer Science, Music Technology, or a cognate discipline. Strong knowledge of digital signal processing and machine learning frameworks such as PyTorch or TensorFlow is required. Experience in MIR, audio analysis, or computational musicology is desirable, and interest in Irish Traditional Music is highly valued.
To apply, submit a personal statement, CV, research proposal, academic transcripts, and two referees’ contact details by email to Dr Behnam Faghih. Non-native English speakers must also provide evidence of English language competency. The closing date is 19 October 2026 at 5 pm Dublin time.
Supervision is by Dr Behnam Faghih with additional guidance from Prof Joseph Timoney.
Funding details
Fully funded for four years through a Maynooth University Doctoral Scholarship. Funding includes a student stipend of €25,000 per annum and annual tuition fee support. The award runs from February 2027 to January 2031, subject to satisfactory annual academic progression.
What's required
Minimum first-class or 2.1 honours in a primary degree, or a relevant Master's degree in Computer Science, Music Technology, or a cognate discipline. Applicants should have a solid understanding of digital signal processing concepts, especially audio analysis such as time-frequency representations, filtering, and feature extraction. Familiarity with machine learning frameworks such as PyTorch or TensorFlow, including neural networks and data-driven modelling, is essential. Prior experience in music information retrieval, audio analysis, or computational musicology is advantageous. An interest in music, particularly Irish Traditional Music, is highly desirable; practical musicianship is a plus but not required. Non-native English speakers must provide evidence of English language competency meeting Maynooth University requirements. Awardees must be resident in Ireland and study full-time at Maynooth University.
How to apply
Prepare a personal statement, CV, research proposal, academic transcripts, and contact details for two referees. Email the application directly to Dr Behnam Faghih at [email protected] with the subject line 'MU Doctoral Scholarship - Department of Computer Science 2027'. Late applications will not be considered.
More information can be found here
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