August 2019:
We are looking for a PostDoc or a PhD Student to work on the ERC project Con Espressione (see below). The position will be for 1 to 1.5 years (PostDoc) or 3 years (PhD student). The PhD student will have the opportunity to complete a doctorate in computer science at the Johannes Kepler University.
The Project:
The Con Espressione Project is funded by the European Research Council (ERC) in the form of an ERC Advanced Grant. Its overall goal is to develop machines that are aware of certain dimensions of expressivity in music. A particular focus of our research is on expressivity in musical performance, and on predictive computational performance models.
More information can be found on the project web page.
Research Focus:
Your research in the project will focus on improving our expressive and reactive/predictive piano accompaniment system (the "ACCompanion", see here for an early short description: <https://arxiv.org/abs/1711.02427>, opens an external URL in a new window), turning it into musical companion that recognises and anticipates expressive intentions by the soloist, learns predictive tempo and performance models on-line, during rehearsal, and combines this with its own internal model of expressive performance in order to create, in real time, a natural and musically expressive accompaniment.
Required Qualifications and Skills:
Practicalities:
We offer a very competitive salary of
for full-time employment (40 hours / week); social security and medical insurance are automatically included.
The PhD student will be employed at a 75% employment level for the first year, which will be raised to 100% if things go well.
Applications should consist of
and whatever else you may consider informative.
Please send your application, via email <gerhard.widmer(at)jku.at>, to Gerhard Widmer.
Please make sure that your motivation letter explains your research background and experience and how this matches the above-mentioned requirements, and demonstrates that you have studied the Con Espressione project pages.
We encourage traditionally underrepresented groups, such as minorities and women, to apply.
This project receives funding from the European Research Council (ERC), opens an external URL in a new window under the European Union's Horizon 2020 research and innovation programme under grant agreement No 670035.