The Institute for Information Law (IViR) at the University of Amsterdam is one of the academic partners in the Horizon Europe Consortium Open Music Europe, consisting of 15 partners in 11 countries. Open Music Europe strives to fill the data gaps identified by 150 music industry stakeholders. We create usable indicators that allow music researchers, music businesses and cultural policymakers to use our data as evidence in more informed institutional, business, or public policies. In the context of this project, the IViR interdisciplinary research team is looking for a postdoctoral researcher interested in music and statistical innovation.

Background
Recorded music has been sold and streamed on global platforms for more than a decade now, which makes the sector one of the most data-rich business environments in the world. Technically, there is a trace of almost every song listened to in any city in the world. In Europe, music stakeholders are small; therefore, they only see a very small part of this global music picture. If there is an increased demand for their catalogue, they do not know if there are more music listeners, their genre is more sought-after, they get traction in better-remunerated territories, currency exchange rates work in their favour, or they reach a new accrual period. We are developing quantitative tools to generalize this information and provide better data and metadata for an economically and socially more sustainable music ecosystem for Europe.

What are you going to do?
We are looking for a candidate familiar with dynamic sampling techniques and can apply the law of big numbers in practice. You will help design an algorithm that will allow us to select a basket of songs to view if the observed mean, median, standard deviation and other statistical properties of their play count and revenue are starting to correlate in the baskets of each other. When they show the same statistical picture, we can be sure that we are selecting songs randomly, without any inherent bias in the selection algorithm, such as the digit distributions of an alphanumeric identifier.

The Successful candidate will work on the challenge to select "typical songs" that well represent the sales prospects, popularity, and use of songs played in a given genre, or country (territory) in a given royalty accounting period (a month). We can access detailed data about where and how many times a song was played and what the financial remuneration was. However, we need to learn how to select a song randomly because we are unfamiliar with the (statistical) population of the songs available on a particular streaming platform or in a country. We can query databases based on the International Standard Recording Code (a unique identifier for recordings) or via a unique identifier given by MusicBrainz, Spotify, YouTube, but we do not yet know how to select randomly. Our problem is similar to creating a representative "basket" of stocks or bonds on the financial markets that represent the market sentiment in a stock or bond market index or selecting in practice participants in a repeating pseudo-panel social sciences survey. In addition to this core task and depening on progress, we will discuss futher work within this Open Music Europe project that the candidate will do.

We are looking for a candidate holding a PhD in for instance quantitative economics, statistics or mathematics who is familiar with the statistical techniques mentioned above and below, and who has an interest in working with music metadata. We are also willing to consider strong candidates holding a master’s degree in such fields, who have a demonstrable interest in research and in working with music metadata. The ideal candidate has the capacity to work independently and in teams, has excellent oral and written communication skills in English, a creative mind and strong critical and analytical skills.

Familiarity with the following techniques is necessary:

• Applied probability calculus to create truly random samples of sound recordings by dynamically discovering the statistical properties (mean, median, percentile counts, standard deviation of counts) of the observations of listening counts in an unknown universe of songs.
• Test your algorithm programmatically in R or Python.
• Understanding the challenges of sparse and skewed data (where most songs in a given period have a 0 play count and the median value is 0), similar to problems encountered with sampling illiquid securities for financial risk quantification of index creation.
• Understanding our ability to understand financial index creation (basket creation) techniques, i.e., choosing a basket of "representative Slovak songs" based on use for consecutive periods, similar to re-balancing the stock market or bond market indexes.

Familiarity with the following will be considered an advantage:

• Measuring the correlation or similarity of song use/royalty time series and clustering such times series to find typical song "histories", like perennial hits, one-summer hits, and upcoming artists' songs.
• Small area statistics or working with statistical techniques designed for small subsamples.
• Music information retrieval in practice, acquiring data from large music APIs or programmatically identifying properties/features of large music corpora.
• Interest in valuing atypical assets like copyright-protected songs or connecting financial performance with social sustainability issues.
• proven experience in independently conducting research.
• Resource Description Framework (RDF) for describing metadata in a standardised way, translating metadata to standard description formats.

We offer an employment contract for 1 year. The preferred starting date is 1 February 2024.

Your salary, depending on your relevant experience on commencement of the employment contract, ranges from €3226 to €5929 (scale 10-11) gross per month on the basis of a full working week of 38 hours.

This sum does not include the 8% holiday allowance and the 8.3% year-end allowance. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement for Dutch Universities (CAO NU) is applicable.

Amsterdam Law School

The Institute for Information Law (IViR), officially established in 1989, is one of the largest research centers in the field of information law in the world. The Institute employs over 35 researchers who are active in an entire spectrum of information society related legal areas: intellectual property law, patents, telecommunications and broadcasting regulation, media law, Internet regulation, advertising law, domain names, freedom of expression, privacy, digital consumer issues, commercial speech, AI, blockchain, et cetera. The Institute engages in cutting-edge research into fundamental and topical aspects of information law, and provides a forum for critical debate about the social, cultural and political aspects of regulating information markets.

Committed, responsible and open-minded. This is how we at the Amsterdam Law School view the role of law in a constantly changing (international) society. With over 5,000 students and 550 staff, we are one of the larger law faculties in the Netherlands. We educate legal professionals who know how to apply the law effectively with the aim of actually contributing to solutions for society. We respond to social developments through innovative and pioneering research. In this way, we always keep in touch with society.

Curious about our organization? Here you can read more about working at the University of Amsterdam.