Stellenangebote

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Stellenausschreibung / Job advertisement

In the Cluster of Excellence Hearing4All at the Carl von Ossietzky University of Oldenburg, Faculty VI, Department of Medical Physics and Acoustics (DMPA), Department of Medical Physics (Prof. Kollmeier), we seek for a

Postdoctoral researcher in computational audiology

(m/f/d, salary according to E13 TV-L,100%)

The position is suitable for part-time work and should be filled as soon as possible for a period of three years. It is aimed at applicants who have extensive knowledge in the fields of statistical audiology, auditory modelling, audiological diagnostics, and hearing aid parameter setting and are willing to participate in the teaching tasks of the DMPA.

As part of a DFG-funded project related to statistical and precision audiology the position will focus on data conditioning and audiological profile analysis for the diagnosis and compensation of hearing impairment. The successful candidate should contribute to strengthening the link between audiological measures and parameter setting of hearing aids. Furthermore, this should ideally include statistical and/or auditory modelling as objective measures supporting the efficiency of hearing diagnostics and optimizing the prescription and fitting of modern hearing devices (see below for a description of the project).

Candidates are expected to have an academic university degree (PhD degree and Master or Diploma degree) in Hearing Technology & Audiology, Biomedical Physics, Engineering Physics, Data Science or a related discipline and have shown their ability to perform excellent scientific work, usually demonstrated by the outstanding quality of their Doctorate/PhD research and a good publication record.

The successful candidate will have extensive knowledge in at least two of the following research fields: statistical modelling, auditory modelling, audiological diagnostics, hearing device parameter selection. A strong interest in interdisciplinary and application-oriented work, familiarity with scientific tools and programming languages, as well as good spoken and written English language skills are required. German language skills are desirable but not obligatory.

The University of Oldenburg is dedicated to increasing the percentage of women in science. Therefore, female candidates are particularly encouraged to apply. In accordance with Lower Saxony regulations (§ 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.

Applications with cover letter, CV in tabular form, publication list, and copies of certificates for academic grades are requested by 22.08.2022 (preferably by E-Mail as one PDF-file) to:

Prof. Dr.rer.nat. Dr.med. Birger Kollmeier, E-mail:

Medizinische Physik und Exzellenzcluster Hearing4all

Universität Oldenburg

D - 26111 Oldenburg

Prof. Kollmeier (birger.kollmeier@uol.de) can be contacted for further questions regarding the position.

Project Summary
Even though hearing impairment is the most common sensory disease with a massive negative impact on approximately 18% of our population, the diagnostics and rehabilitation approaches with hearing devices are still limited, e. g., the restoration of normal speech perception in everyday noisy conditions is still incomplete - primarily because of the scattered empirical knowledge about the reduced speech perception and the limited individual benefit from a hearing device without a systematic data analysis approach. The current project addresses this problem in a statistical, machine-learning-guided way that combines basic science, statistical analysis, and clinical audiology independent from the test language employed: By building up an exchangeable, easily accessible, and expandable database of audiological diagnostic measures and specialized, language-independent tests applied to a clinical population, we will analyze the causes and consequences of hearing impairment in a precise and individualized manner. Statistical and machine-learning methods will be used to identify a minimum set of measures needed for the diagnosis of a given hearing disorder with high certainty. This should lead to statistically motivated auditory profiles which will be linked to individualized treatment recommendations and fitting of hearing devices that are verified using an idealized, laboratory-based "open master hearing aid" (Grimm et al., 2006) in comparison to the benefit provided by the patient´s own device. Hence, using only a few specific audiological tests in parameter selection and assessing the benefit from hearing devices, a more comprehensive and theory-grounded way of hearing rehabilitation will be reached, thus increasing the efficiency and acceptance of hearing devices. Generally, this statistical audiology approach will lead to a precise and efficient diagnostics and an optimized prescription and fitting of modern hearing devices.

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(Stand: 28.04.2022)