AdresseState-of-the-art medicine and nursing care you can rely on! - Since being
founded in 1815, Universitätsklinikum Erlangen has offered medicine of the
highest standard and incorporated the latest insights from medical research
and state-of-the-art equipment into diagnosis and therapy. More than 9.400
employees from approximately 50 different occupations are needed to provide
the wide range of services offered by Universitätsklinikum Erlangen, ensuring
our patients receive the very best care around the clock.
founded in 1815, Universitätsklinikum Erlangen has offered medicine of the
highest standard and incorporated the latest insights from medical research
and state-of-the-art equipment into diagnosis and therapy. More than 9.400
employees from approximately 50 different occupations are needed to provide
the wide range of services offered by Universitätsklinikum Erlangen, ensuring
our patients receive the very best care around the clock.
Research Assistant (PhD Student - Dr.-Ing.) or Postdoctoral Researcher (f/m/x)
for the Department of phoniatrics and Pediatric audiology of the Ear, nose and throat clinic, head an neck surgery
Your tasks:
Deep learning networks & feature analysis to understand speech-motor-control processes in hearing impaired patients
Speech production is a highly complex process involving the coordination of the respiratory, laryngeal, and oral motor systems as well as a large network of brain regions being involved in motor, somatosensory, and auditory tasks. The auditory feedback plays an important role in tuning the so called speech motor control (SMC) system. It is well known that auditory deprivation, due to hearing loss, may result in significant deteriorations of speech processes.
The central objective in this study on SMC in hearing impaired patients is the identification of the impact of disturbed auditory input on audio-kinesthetic processes. By applying and analyzing multi-sensor based data including laryngeal high-speed imaging, electroencephalography (EEG), and the acoustic voice signal the project aims to delineate the interaction between auditory perception and motoric.
Your tasks: (1) Develop and find machine learning approaches to reveal differences in underlying SMC parameters in high-speed videos, EEG, and acoustic data between normal hearing subjects and hearing impaired. This yields clinical relevant parameters (feature analysis) which represent SMC deterioration. (2) Support execution of the experiments with another PhD student within the project.
Supervision is enabled by the membership of Prof. Döllinger (supervisor) at the Technische Fakultät (Department Informatik). Our team is highly interdisciplinary and has several collaborations with technical and natural science chairs at FAU. In this project we cooperate with Prof. Hoppe (ENT hospital), Prof. Nöth (LS Informatik 9) and Dr. Abur (Netherlands) We foster personal development and exposure to an international, cutting-edge environment.
Essential experience/qualifications:
Comments:
We offer:
Further information:
Universitätsklinikum Erlangen
Prof. Dr. Michael Döllinger
Tel: +49 9131 85 33814
Applications:
Prof. Dr. Michael Döllinger
Department of Phoniatricsand Peadiatric audiology
Universitätsklinikum Erlangen
Waldstraße 1
D-91054 Erlangen
Deep learning networks & feature analysis to understand speech-motor-control processes in hearing impaired patients
Speech production is a highly complex process involving the coordination of the respiratory, laryngeal, and oral motor systems as well as a large network of brain regions being involved in motor, somatosensory, and auditory tasks. The auditory feedback plays an important role in tuning the so called speech motor control (SMC) system. It is well known that auditory deprivation, due to hearing loss, may result in significant deteriorations of speech processes.
The central objective in this study on SMC in hearing impaired patients is the identification of the impact of disturbed auditory input on audio-kinesthetic processes. By applying and analyzing multi-sensor based data including laryngeal high-speed imaging, electroencephalography (EEG), and the acoustic voice signal the project aims to delineate the interaction between auditory perception and motoric.
Your tasks: (1) Develop and find machine learning approaches to reveal differences in underlying SMC parameters in high-speed videos, EEG, and acoustic data between normal hearing subjects and hearing impaired. This yields clinical relevant parameters (feature analysis) which represent SMC deterioration. (2) Support execution of the experiments with another PhD student within the project.
Supervision is enabled by the membership of Prof. Döllinger (supervisor) at the Technische Fakultät (Department Informatik). Our team is highly interdisciplinary and has several collaborations with technical and natural science chairs at FAU. In this project we cooperate with Prof. Hoppe (ENT hospital), Prof. Nöth (LS Informatik 9) and Dr. Abur (Netherlands) We foster personal development and exposure to an international, cutting-edge environment.
Essential experience/qualifications:
- M.Sc. / PhD in artificial intelligence, data science, computer science, mathematics, medical engineering, computational engineering, or similar
- Profound knowledge in machine learning methods (e.g. deep learning, …)
- Programming skills in Python and / or similar
- Structured and independent working practice, good communication and English skills
Comments:
- Intended pay grade depending on qualifications and personal
requirements according to TV-L E13 - The position is initially for a fixed term of three years.
- The position is full time; the starting date is as soon as possible.
- Application deadline ends on 14.12.2022
- Notes for applicants (f/m/x)
We offer:
- A secure, interesting job in a motivated, open-minded team
- Careful and qualified training and support for the new challenge
- A varied and responsible field of activity with individual development opportunities
- Comprehensive health promotion offers
- All benefits of the public service incl. supplementary pension plan of the Versorgungsanstalt des Bundes und der Länder (VBL) (Federal and State Government Employees' Retirement Fund)
- Family-friendly environment and, where appropriate, places in a dormitory
- Crediting of work experience possible
Further information:
Universitätsklinikum Erlangen
Prof. Dr. Michael Döllinger
Tel: +49 9131 85 33814
Applications:
Prof. Dr. Michael Döllinger
Department of Phoniatricsand Peadiatric audiology
Universitätsklinikum Erlangen
Waldstraße 1
D-91054 Erlangen
