This PhD project explores the development of advanced Music Information Retrieval (MIR) techniques tailored to Irish Traditional Music (ITM), a rich and expressive musical tradition characterised by complex ornamentation, diverse regional styles, and predominantly oral transmission. The project aims to bridge traditional signal processing approaches with modern deep learning methods to automatically analyse and understand key musical features in ITM recordings.

The successful candidate will design and implement algorithms capable of extracting musically meaningful information, such as note transcription, tune-type classification (e.g., reels, jigs, hornpipes), ornamentation detection, instrument recognition, and structural form analysis. Additional focus will be placed on identifying stylistic variations across performers and regions, contributing to a deeper computational understanding of ITM performance practices. The project will involve working with real-world audio datasets, addressing challenges such as performance variability, polyphony, and the nuanced expressivity that defines the genre. Candidates will have the opportunity to experiment with a range of techniques, from time-frequency analysis and probabilistic models to state-of-the-art machine learning and deep neural networks.

This research has applications in digital music archives, intelligent music recommendation systems, music education tools, and cultural heritage preservation. The project is well-suited to candidates with a background in computer science, audio signal processing, machine learning, or related fields, and an interest in music.

The PhD offers a unique opportunity to contribute to technological innovation and the preservation and understanding of Irish musical heritage.

Key objectives

• Develop robust MIR algorithms for Irish Traditional Music (ITM)
• Automatic transcription and feature extraction
• Tune type and structural classification
• Ornamentation and stylistic analysis
• Instrument recognition and timbre modelling
• Regional and performer style modelling

Ethical and Resource Considerations

1. Ethical Considerations

• Cultural Sensitivity and Representation - Irish Traditional Music (ITM) is a living cultural heritage with strong community ownership and identity. The project must ensure respectful representation of musical practices, avoiding reductive or misleading computational interpretations of style, ornamentation, or regional identity.
• Data Ownership and Consent - Audio recordings may involve copyrighted material or performances by identifiable musicians. Appropriate permissions, licensing, and attribution must be ensured, particularly when using archival or community-sourced data.
• Explainability and Transparency - Especially with deep learning approaches, efforts should be made to ensure the interpretability of models, so that musical insights are meaningful and trustworthy for both researchers and practitioners.
• Impact on Musical Practice - Consideration should be given to how computational tools may influence learning, performance, or perception of ITM, ensuring they support rather than undermine traditional practices.

2. Resource Considerations

• Dataset Availability and Annotation - High-quality annotated datasets for ITM are limited. The project may require significant effort in data collection, curation, and annotation, potentially involving collaboration with musicians and domain experts.
• Interdisciplinary Expertise - Input from ethnomusicologists, musicians, and domain experts may be necessary to ensure musically meaningful interpretations and evaluations.
• Sustainability and Reproducibility - Ensuring that datasets, code, and models are well-documented and shareable will be important for long-term impact and reuse by the research community.

Value of Maynooth University Doctoral Scholarship Award

The award is fully funded for four years, commencing February 2027 and running to completion in January 2031.

The following funding is available for the successful applicant:

• Student stipend: €25,000 per annum
• Annual Tuition Fees Support

Duration of the Maynooth University Doctoral Scholarship Award

The scholarship is awarded for four years of full-time study, subject to satisfactory annual academic progression.

Role of the student

The project topic is pre-specified with defined deliverables as outlined above.

The project is organised around several tasks, including data collection and preparation, data processing and classification, data annotation, documentation, developing algorithms and Python libraries, publications, and presentations.

The PhD duties will involve working on these tasks under the supervision of Dr Behnam Faghih (with additional guidance from other project leads).

The study is subject to the terms and conditions of MU Doctoral Scholarships and must be completed in accordance with the requirements set out in the Research Student Programme Stages as outlined in the MU Regulations for Postgraduate Research Degrees.

Mode of Study

Awardees must be resident in Ireland and available to pursue their programme of research on a full-time basis at Maynooth University for four years.

The successful candidate’s research programme will be under the general supervision of their nominated supervisors, who will specify study times, research times, vacation periods and other operational requirements.

Eligibility Criteria

Minimum first class or 2.1 honours in their primary degree, or have a relevant Master’s degree in Computer Science, Music Technology or cognate discipline.

Essential Criteria

• Solid understanding of digital signal processing concepts, particularly in audio analysis (e.g., time-frequency representations, filtering, feature extraction).
• Familiarity with machine learning techniques and frameworks (e.g., PyTorch, TensorFlow), including experience with neural networks and data-driven modelling.

Desirable Criteria

• Prior experience or knowledge in MIR, 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.

Application and Selection Process

Please include the following in your application:

• Personal statement (max. 400 words)
• Curriculum Vitae
• Research proposal (max. 2,500 words)
• Academic transcripts
• Names and contact details of two referees

Applicants who are non-native speakers of the English language must provide written evidence of competency in the English language that satisfies the programme-specific requirements set out by the Maynooth University International Office.

All eligible candidates will be considered for open positions. Applicants may be shortlisted for interview and, if so, will be contacted directly by the Department. Late applications will not be considered.

Informal queries can be sent to Behnam.Faghih@mu.ie

Closing Date: Please apply by 5 pm on Monday 19th October 2026, Dublin time.

Please submit applications by email directly to Dr Behnam Faghih at Behnam.Faghih@mu.ie. Please title the subject of your email application as ‘MU Doctoral Scholarship - Department of Computer Science 2027’.

This PhD project explores the development of advanced Music Information Retrieval (MIR) techniques tailored to Irish Traditional Music (ITM), a rich and expressive musical tradition characterised by complex ornamentation, diverse regional styles, and predominantly oral transmission. The project aims to bridge traditional signal processing approaches with modern deep learning methods to automatically analyse and understand key musical features in ITM recordings.

The successful candidate will design and implement algorithms capable of extracting musically meaningful information, such as note transcription, tune-type classification (e.g., reels, jigs, hornpipes), ornamentation detection, instrument recognition, and structural form analysis. Additional focus will be placed on identifying stylistic variations across performers and regions, contributing to a deeper computational understanding of ITM performance practices. The project will involve working with real-world audio datasets, addressing challenges such as performance variability, polyphony, and the nuanced expressivity that defines the genre. Candidates will have the opportunity to experiment with a range of techniques, from time-frequency analysis and probabilistic models to state-of-the-art machine learning and deep neural networks.

This research has applications in digital music archives, intelligent music recommendation systems, music education tools, and cultural heritage preservation. The project is well-suited to candidates with a background in computer science, audio signal processing, machine learning, or related fields, and an interest in music.

The PhD offers a unique opportunity to contribute to technological innovation and the preservation and understanding of Irish musical heritage.

Key objectives

• Develop robust MIR algorithms for Irish Traditional Music (ITM)
• Automatic transcription and feature extraction
• Tune type and structural classification
• Ornamentation and stylistic analysis
• Instrument recognition and timbre modelling
• Regional and performer style modelling

Ethical and Resource Considerations

1. Ethical Considerations

• Cultural Sensitivity and Representation - Irish Traditional Music (ITM) is a living cultural heritage with strong community ownership and identity. The project must ensure respectful representation of musical practices, avoiding reductive or misleading computational interpretations of style, ornamentation, or regional identity.
• Data Ownership and Consent - Audio recordings may involve copyrighted material or performances by identifiable musicians. Appropriate permissions, licensing, and attribution must be ensured, particularly when using archival or community-sourced data.
• Explainability and Transparency - Especially with deep learning approaches, efforts should be made to ensure the interpretability of models, so that musical insights are meaningful and trustworthy for both researchers and practitioners.
• Impact on Musical Practice - Consideration should be given to how computational tools may influence learning, performance, or perception of ITM, ensuring they support rather than undermine traditional practices.

2. Resource Considerations

• Dataset Availability and Annotation - High-quality annotated datasets for ITM are limited. The project may require significant effort in data collection, curation, and annotation, potentially involving collaboration with musicians and domain experts.
• Interdisciplinary Expertise - Input from ethnomusicologists, musicians, and domain experts may be necessary to ensure musically meaningful interpretations and evaluations.
• Sustainability and Reproducibility - Ensuring that datasets, code, and models are well-documented and shareable will be important for long-term impact and reuse by the research community.

Value of Maynooth University Doctoral Scholarship Award

The award is fully funded for four years, commencing February 2027 and running to completion in January 2031.

The following funding is available for the successful applicant:

• Student stipend: €25,000 per annum
• Annual Tuition Fees Support

Duration of the Maynooth University Doctoral Scholarship Award

The scholarship is awarded for four years of full-time study, subject to satisfactory annual academic progression.

Role of the student

The project topic is pre-specified with defined deliverables as outlined above.

The project is organised around several tasks, including data collection and preparation, data processing and classification, data annotation, documentation, developing algorithms and Python libraries, publications, and presentations.

The PhD duties will involve working on these tasks under the supervision of Dr Behnam Faghih (with additional guidance from other project leads).

The study is subject to the terms and conditions of MU Doctoral Scholarships and must be completed in accordance with the requirements set out in the Research Student Programme Stages as outlined in the MU Regulations for Postgraduate Research Degrees.

Mode of Study

Awardees must be resident in Ireland and available to pursue their programme of research on a full-time basis at Maynooth University for four years.

The successful candidate’s research programme will be under the general supervision of their nominated supervisors, who will specify study times, research times, vacation periods and other operational requirements.

Eligibility Criteria

Minimum first class or 2.1 honours in their primary degree, or have a relevant Master’s degree in Computer Science, Music Technology or cognate discipline.

Essential Criteria

• Solid understanding of digital signal processing concepts, particularly in audio analysis (e.g., time-frequency representations, filtering, feature extraction).
• Familiarity with machine learning techniques and frameworks (e.g., PyTorch, TensorFlow), including experience with neural networks and data-driven modelling.

Desirable Criteria

• Prior experience or knowledge in MIR, 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.

Application and Selection Process

Please include the following in your application:

• Personal statement (max. 400 words)
• Curriculum Vitae
• Research proposal (max. 2,500 words)
• Academic transcripts
• Names and contact details of two referees

Applicants who are non-native speakers of the English language must provide written evidence of competency in the English language that satisfies the programme-specific requirements set out by the Maynooth University International Office.

All eligible candidates will be considered for open positions. Applicants may be shortlisted for interview and, if so, will be contacted directly by the Department. Late applications will not be considered.

Informal queries can be sent to Behnam.Faghih@mu.ie

Closing Date: Please apply by 5 pm on Monday 19th October 2026, Dublin time.

Please submit applications by email directly to Dr Behnam Faghih at Behnam.Faghih@mu.ie. Please title the subject of your email application as ‘MU Doctoral Scholarship - Department of Computer Science 2027’.