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.

• A master's degree in electrical engineering, physics, computer science or similar (this is mandatory).
• A record that shows specific interest in any or more of the following fields: signal processing, (Bayesian) machine learning, professional software development.
• Good written and spoken command of English (C1 level or better).
• A team player attitude, willingness to work hard and know how to have fun at it.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale 27 (min. €2,541 max. €3,247).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.