Research Associate “Speech and audio foundation models for hearing aid processing”

About us

The School VI of Medicine and Health Sciences comprises the fields of human medicine, medical physics and acoustics, neurosciences, psychology and health services research. Together with the four regional hospitals, School VI forms the University Medicine Oldenburg. Furthermore, the university cooperates closely with the University Medicine of the University of Groningen.

The research associate position is embedded within the Signal Processing Division (http://www.sigproc.uni-oldenburg.de) and the Cluster of Excellence Hearing4all.connects. The main activities of the Signal Processing Division center around signal processing for acoustical and biomedical applications, with a focus on hearing aids and speech communication devices. More specifically, research topics in the areas of microphone array processing, speech enhancement and acoustic scene analysis are addressed, using a combination of model-based statistical signal processing techniques and data-driven machine learning methods. The Signal Processing Division has access to excellent high-performance computing facilities, measurement equipment and labs, e.g., a unique lab with variable acoustics (see demos on YouTube channel: https://www.youtube.com/@signalprocessingunioldenbu1018)  

Your tasks

• conduct research on the use of speech and audio foundation models for real-time sound processing algorithms for hearing aids (e.g., acoustic scene analysis, noise reduction, speaker separation), including algorithm design, implementation, and experimental validation
• conduct research on distributed deep-learning-based algorithms for hearing aids, e.g., by offloading computationally intensive tasks to a more powerful external device such as a smartphone 
• close collaboration with cooperation partners of the project
• publish research results in scientific journals
• active participation in international conferences
• active participation in research meetings and seminars of the Cluster of Excellence Hearing4all.connects and the Department of Medical Physics and Acoustics
• supervise students 

Your profile

Required qualifications

• academic university degree (Master or equivalent) in electrical engineering, computer science, applied physics, or a related discipline
• demonstrated ability to produce excellent scientific work, typically through the outstanding quality of the doctoral thesis
• excellent publication record
• extensive knowledge and experience in speech/audio signal processing and machine learning
• excellent programming skills (e.g., python, Matlab)
• excellent written and spoken English language skills

Preferred qualifications:

• experience with speech and audio foundation models
• experience with signal processing algorithms for hearing devices 
• experience with distributed signal processing or machine learning methods
• good communication skills
• ability to work in a team

We offer

• Integration into a dedicated team and an excellent scientific environment
• Access to excellent acoustical labs and computing facilities
• Opportunities to collaborate within the interdisciplinary Cluster of Excellence Hearing4all.connects
• Payment in accordance with collective bargaining law (special annual payment, company pension scheme, asset-related benefits) incl. 30 days annual leave
• Support and guidance during your onboarding phase
• A family-friendly environment with flexible working hours (flexitime) and the possibility of pro-rata mobile work
• An extensive and free further education programme as well as programmes geared toward the promotion of early-career researchers (https://uol.de/en/school6/early-career)

Our standards

The University of Oldenburg is dedicated to increase the percentage of female employees in the field of science. Therefore, female candidates are strongly encouraged to apply. In accordance to § 21 Section 3 NHG, female candidates with equal qualifications will be preferentially considered. Applicants with disabilities will be given preference in case of equal qualification.

Contact

For further information, please contact Prof. Dr. Simon Doclo (; http://www.sigproc.uni-oldenburg.de).