- Are you passionate about computing (AI, ML) and about music (and do you have a strong background in both of these)?
- Do you want to research new methods for modeling musical skills and processes with machine learning and computational models?
- Do you want to design and contribute to new, creative uses of computational models in musicology, music education, and artistic contexts?
- Do you want to be part of a leading MIR research team and a large, high-profile research project?
Then consider applying for one of the currently open PhD student positions in our new ERC project Whither Music?, opens an external URL in a new window !
The positions are fully funded, for full-time research work exclusively on the project, with a guaranteed duration of 3 years and possibility of extension, if necessary or desired. The PhD students will have the opportunity to complete a doctorate in computer science at the Johannes Kepler University.
The Whither Music? , opens an external URL in a new windowproject, opens an external URL in a new window is funded by the European Research Council (ERC), opens an external URL in a new window in the form of an ERC Advanced Grant, opens an external URL in a new window. Its overall goal is to establish model-based computer simulation (via methods of AI, (deep) Machine Learning and probabilistic modelling) as a viable methodology for asking questions about music and musical possibilities. To this end, we aim at developing musically informed, structured approaches to computational modeling, combining extensive machine learning with musical knowledge and carefully chosen representations and model architectures. More information can be found on the Project Web Page.
Required Qualifications and Skills:
- Completed master's degree in Computer Science / AI / ML or a related field, with a demonstrated focus on Music, MIR, Musical Informatics (e.g., in a pertinent master's thesis)
- Strong background and interest in (tonal/classical) music
- Experience in Machine Learning and Deep Neural Networks
- A good understanding of probabilistic models and inference
- Interest in computational models of music and music cognition
- .. and, most importantly: creativity, motivation, intellectual independence.
We offer a highly competitive salary of approx. EUR 42,000.- per year (approx. 29,000.- after taxes) for a full-time employment (40 hours / week); social security and medical insurance are included. The PhD student will be employed at a 75% employment and salary level for the first half year, which will be raised to 100% if things go well. The start date would optimally be in early 2022, but we have some flexibility.
Applications should consist of
- a motivation letter
- a curriculum vitae
- an electronic copy of your master thesis
- a list of publications (if applicable)
- a reference letter
- and, most importantly: a Research Exposé (3-5 pages) that sketches one or two challenging research questions that you would like to work on in a PhD thesis, and explains how these might be aligned with the grand goals of the Whither Music? project. In particular, indicate which modeling domain (generation, performance, perception/expectancy) you are most interested in, and try to think of some creative ideas for possible model-based simulation experiments or demonstration prototypes. (For your orientation, here is a 1-page "project map", opens a file that you may want to relate your ideas to.) If you are unsure about the project's goals or whether a research idea might fit the project, feel free to contact Gerhard Widmer, opens an external URL in a new window with more specific questions.
Take your time thinking about and writing this! We will not make candidate selection decisions before late 2021.
(And note: This exposé will not necessarily bind you to a specific PhD topic; it is simply a way to get you thinking about the project, and for us to learn a bit about your research interests, creativity, knowledge, and writing style.)
Please send applications or any questions to Gerhard Widmer, via email to <email@example.com>.
We encourage traditionally underrepresented groups, such as minorities and women, to apply.