We are seeking a PhD candidate for the project 'InDeep: Interpreting Deep Learning Models for Text and Sound Methods and Applications' (NWO NWA 1292.19.399). This ambitious project aims to develop, apply, and fine-tune techniques to make modern deep learning models for text, speech, and music more transparent.

In this PhD position you can combine insights from audio analysis and deep learning to help musicians play what they love. If you are excited about doing this kind of research in an interdisciplinary environment with smart and friendly colleagues and a strong industrial collaboration, then you may want to join us.

The project is funded by an NWO Dutch Research Agenda grant to a consortium led by Dr. Willem Zuidema of the Institute for Logic, Language, and Computation (ILLC) at the University of Amsterdam (UvA). The consortium also includes Tilburg University, the University of Groningen, Radboud University, and Vrije Universiteit Amsterdam as academic partners and KPN, AIGent, TNO, Textkernel, Chordify, GlobalTextware, Deloitte, and Floodtags as industrial partners.

What are you going to do

Can we deduce how hard a song is to play purely from the audio? Is the difficulty scale absolute or should it be individual? This project aims to use unsupervised or semi-supervised learning methods to achieve a measure of a song "playability": the level of music expertise necessary to be able to play along with a given song on a given instrument. Initially, the goal is to develop a tool that aspiring musicians will be able to use to choose songs on the Chordify platform that are on the right difficulty level for them, as well as a tool that Chordify can use to evaluate the capabilities of its users. As the project advances, we hope to expand the research to help music teachers choose appropriate songs for learning and examination.

The technological focus will be on state-of-the-art Explainable AI (XAI) techniques in deep-learning models, as part of a larger consortium including experts in applications of deep learning to natural language processing and speech recognition. The consortium is particularly interested in the potential of attribution methods to build a bridge between deep-learning models and more musicologically-inspired models of musical structure in music information retrieval (MIR).

The PhD candidate will have a four-person supervision team including Prof. Henkjan Honing (music cognition), Dr Willem Zuidema (NLP, Interpretability, cognitive modelling), Dr John Ashley Burgoyne (music information retrieval), and Dr Jonathan Driedger (director of research at Chordify).

Tasks and responsibilities:

• Independently carrying out research, including writing and publishing three to four peer-reviewed articles.
  
  
• Submitting a PhD thesis within the period of appointment.
  
  
• Participating in the PhD programme of the ILLC.
  
  
• Participating in and contributing to the organisation of research activities and events at the ILLC, such as workshops and colloquia.
  
  
• Making a small contribution to the ILLC's educational mission by working as a teaching assistant for courses in your area of expertise and by assisting with the supervision of student research projects.
  
  
• Regularly presenting research results at international workshops and conferences, and publishing them in conference proceedings and journals.
  
  

We expect you to have:

• a completed master's degree in music theory, cognitive science, machine learning, psychometrics, or a related field
  
  
• a serious interest in pursuing applied music research
  
  
• a serious interest in music (with a basic knowledge of Western pop harmony and music education desirable but not required)
  
  
• an excellent academic track record
  
  
• working knowledge of state-of-the art techniques in deep learning (with knowledge of explainable AI techniques desirable but not required)
  
  
• good programming skills and experience with Python or R and PyTorch or TensorFlow
  
  
• full professional proficiency in spoken and written English
  
  
• the ability to finish the PhD thesis in four years (i.e., good skills in planning, taking initiative, and academic writing)
  
  

You may apply if you have not yet completed your master's degree only if you provide a signed letter from your supervisor stating that you will graduate before 1 November 2022.

Please note that knowledge of the Dutch language is not required for this position, nor is it required for being able to live in Amsterdam. However, if you wish, as a PhD candidate at the ILLC you will have the opportunity to attend Dutch language classes.

If you already hold a doctorate/PhD or are working towards obtaining a similar degree elsewhere, you will not be admitted to a doctoral programme at the UvA.

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is 1 November 2022. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between € 2,443 to € 3,122 (scale P). This does not include 8% holiday allowance and 8,3% year-end allowance. The Collective Labour Agreement of Universities of the Netherlands is applicable.

Faculty of Science

The University of Amsterdam (UvA) is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 30,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

The Institute for Logic, Language and Computation (ILLC) is a research institute at the UvA in which researchers from the Faculty of Science and the Faculty of Humanities. collaborate. Its central research area is the study of fundamental principles of encoding, transmission and comprehension of information. Research at ILLC is interdisciplinary, and aims at bringing together insights from various disciplines concerned with information and information processing, such as logic, mathematics, computer science, linguistics, natural language processing, cognitive science, artificial intelligence, music cognition, and philosophy.