In this PhD project you will develop personalized audio processing algorithms that run on portable devices. We take inspiration from how the brain works. This research project requires a multidisciplinary approach, based on probabilistic (Bayesian) machine learning, computational neuroscience and software development. Please see this youtube presentation
on Natural Artificial Intelligence for more information about our research.Job Description
This PhD project is funded by the ROBUST
program that aims to develop trustworthy AI tools for today's big societal challenges. One of these challenges concerns improving the participation of hearing-impaired persons in challenging work and social settings. In this PhD project, you will develop Bayesian AI methods that enable hearing-impaired persons to improve (e.g., personalize) their hearing device algorithm through in-situ interactions with an intelligent agent. Your algorithms will be implemented on portable devices and operate under computational and energy-consumption constraints.
An important part of the PhD research will be devoted to contributing to RxInfer (http://rxinfer.ml
), which is a toolbox-under-development for automating real-time Bayesian inference. Hence, your work will partly consist of developing and coding fundamental (Bayesian) AI tools, and partly on applying these tools to audio processing applications. Therefore, for a perfect fit with this position, you should have a keen interest and background in quality software development.
You will work in the BIASlab
team in the Electrical Engineering department at TU/e. This lab focuses its research activities on transferring a leading physics/neuroscience-based theory about computation in the brain, the Free Energy Principle (FEP), to practical use in engineered devices such as augmented hearing devices. During this project you will closely collaborate with other BIASlab researchers, as well as with project team members at the Human Technology Interaction
lab, and with our industrial hearing device partner GN Hearing.
Key areas of interest include Bayesian machine learning, probabilistic graphical models (factor graphs), computational neurosciences, signal processing and software development.