PhD Position in Music Computing

Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application deadline: 31 March 2026

Are you fascinated by music and computing? Join the Music Information Computing Group as a PhD candidate to work on computational modelling of musical style - a key challenge in Music Information Retrieval (MIR). Style understanding drives user modelling, cultural heritage preservation, music categorization, transcription, recommendation and historical analysis. The project aims at explainable models with broad musical coverage to deepen insights into musical style, perception and preferences.

Your job

The core aim of the project is to design explainable computational models that drive high-impact applications in Music Information Retrieval (MIR), while advancing our computational understanding of musical style: what defines it, what are its elements, how is it structured and perceived, how does it vary?

The project will build on various theories. A promising starting point is Leonard B. Meyer’s theory of musical style, which defines style as a replication of patterning. A central challenge in this approach is to identify structural elements of music as instances of patterns. What these patterns are differs culturally and historically. The body of literature on topic theory (founded by Leonard Ratner) offers a point of departure to identify such patterns and to understand the way in which these are replicated and perceived. For example, a fragment of music could allude to a ’fanfare’, or to a ’horn call’, to just mention two examples out of many. These kinds of patterns have many occurrences throughout music history. Can we design computational models for such topics? How do topics function in game music and film music? How are different musical styles interconnected by occurrences of topics?

We also envision to connect with current understanding of music cognition, specifically building on insights on musical memory. There is a class of modular cognitive models of music processing that include a ’musical lexicon’ as one of the cognitive modules. This ’musical lexicon’ determines for a given listener what musical patterns can be recognized. Understanding of this personalized music perception plays a role in user modelling for interactive music systems.

An important challenge lies in designing models that go beyond merely achieving high accuracy in classifying musical styles or genres, or in detecting specific musical patterns. The process of modelling facilitates the understanding of the patterns through a computational lens. This calls for strong expertise in computational methods, machine learning, and data modelling combined with solid knowledge of music. We particularly aim to cover a broad range of musical traditions and cultures world-wide, both contemporary and historical.

In this project you will:

• prepare data sets representing a wide range of musical styles, including both audio and symbolic formats;
• design appropriate data structures for representation of music;
• design computational detectors for various musical patterns;
• design, implement and evaluate computational models of musical style;
• apply these models in several case studies.

Furthermore, you will communicate results in academic presentations and publications, and ultimately in a PhD thesis. During the project, you will expand your academic network. A moderate percentage of the time will be spent on teaching tasks within the department, providing you with the opportunity to gain experience in teaching.

Your qualities

We are looking for a new colleague who meets multiple of the following criteria:

• a Master’s degree in computing science, information science, or a closely related field;
• strong programming skills for implementing and testing computational models;
• broad interest in music, including historical and non-Western musics;
• knowledge of music theory, music notation, music history, and audio processing;
• willingness to engage deeply with topic theory;
• curiosity, independence, eagerness to learn;
• strong written and verbal communication skills in English.

Our offer

• a position for 18 months, with an extension to a total of four years upon successful assessment in the first 18 months;
• a gross monthly salary starting at € 3,059 in the first year and increasing annually up to €3,881 in the fourth year in the case of full-time employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment laid down in the CAO NU, Utrecht University also offers a range of its own schemes for employees. This includes arrangements for professional development , various types of leave, and options for sports and cultural activities . You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working at Utrecht University .

About us

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University , the various disciplines collaborate intensively towards major strategic themes . Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability. Sharing science, shaping tomorrow .

Working at the Faculty of Science means bringing together inspiring people across disciplines and with a variety of perspectives and backgrounds. The Faculty has six departments: Biology, Pharmaceutical Sciences, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure. Visit us on and discover how you can become part of our community.

The Department of Information and Computing Sciences is nationally and internationally known for its research in computer science and information science. The Department provides and contributes to the undergraduate programmes in Computer Science, Information Science, and Artificial Intelligence and a number of research Master’s programmes in these fields. It employs over 200 people in four divisions: Algorithms, AI & Data Science, Software and Interaction. The atmosphere is collegial and informal.

Research of the Music Information Computing group ( Professor Anja Volk ) lies at the intersection of computer and information sciences, mathematics, and music. We develop computational models for musical structures to understand music as a fundamental human trait, and apply these musical structures in novel interaction technologies spanning areas such as music information retrieval; cultural heritage; digital musicology; music recommendation; music and AI; music education; and health, well-being and inclusion.

More information

For more information, please contact dr. Peter van Kranenburg at p.vankranenburguu.nl .

Do you have a question about the application procedure? Please send an email to science.recruitmentuu.nl .

As Utrecht University, we want to be a home for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.

Knowledge security screening can be part of the selection procedures of academic staff. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology.

If you are enthusiastic about this position, just apply via the "Apply now" button! Please enclose:

• your letter of motivation;
• your curriculum vitae;
• your academic transcripts (certified records of your grades) for both your BSc and MSc.
• the names, telephone numbers, and email addresses of at least two references;
• a copy of your MSc thesis.

If this specific opportunity isn’t for you, but you know someone else who may be interested, please forward this vacancy to them.

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The application deadline is 1 April 2026.