Research Assistant (PhD Student - Dr.-Ing.) or Postdoctoral Researcher, (TV-L E13 -100%)

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Research Assistant (PhD Student - Dr.-Ing.) or Postdoctoral Researcher, (TV-L E13 -100%)
Topic: 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
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.

What we expect:
• 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

Additional Information:
Time frame: as soon as possible
Duration: 3 years (= duration of project, funded by DFG), 100% TVL-E13

Please send your application (CV, certificates, skills) to
Prof. Dr.-Ing. Michael Döllinger, Dipl.-Math. (, Tel. 09131- 85 33814