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Archiv-Übersicht     Angebot Nr. 14048

Angebotsdatum: 20. Oktober 2020
Art der Stelle: Doktorarbeit
Fachgebiet: Humanmedizin > Anatomie
Titel des Themas: Multi-sensor (image and acoustic) data analysis by Machine Learning methodologies

Institut: FAU Erlangen-Nürnberg
Adresse:
Prof. Michael Döllinger
Waldstrasse 1
91054 Erlangen
Tel.: 09131-8533814   Fax.:
Bundesland:
Homepage: http://https://www.ki.fau.de/speakers/prof-dr-ing-michael-doellinger/
E-Mail Kontakt: mail

Beschreibung: Your tasks: The goal of the project is the development of a clinically usable software tool to judge voice quality and treatment based on multi-sensor data. Research on methods for classification of voice quality will provide the methodical basis for the software. Data will stem from clinical high-speed video recordings (4000fps), acoustic and patient data. The project has the following main goals by applying machine and deep learning techniques:
1. objectively grade speech disorders;
2. objectively assess and quantify treatment progress;
3. implement the developed machine learning algorithms in a graphical user interface that can then be used by other researchers and clinicians to finally transfer these machine learning methods to clinical application to provide a computer based quantitative and visual presentation of the clinical status for assessment of the clinical picture of speech disorders and treatment progress.
Supervision is enabled by the membership of Prof. Döllinger (supervisor) at the Technische Fakultät (Department Informatik). Our team is highly interdisciplinary and our division has several collaborations with technical and natural science chairs at FAU (e.g. LS Informatik 9, LS Informatik 10, LS Biophysik, LS AM - Kont. Optimierung, LS Sensorik) and with internationally high-ranking universities (UCLA; McGill, Sydney). We foster personal development and exposure to an international, cutting-edge environment.
Methoden: Machine Learning, Deep Learning,...
Programmieren in C# und Python
Anfangsdatum: 1. Januar 2021
Geschätzte Dauer: 36 Monate
Bezahlung: TVL E13 - 100%
Papers: P. Schlegel, S. Kniesburges, S. Dürr, A. Schützenberger, M. Döllinger. Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings. Scientific Reports, 10(1):10517; 2020.
A. Kist, M. Döllinger. Efficient biomedical image segmentation on EdgeTPUs at point of care. IEEE Access, vol. 8, art. 9151951:139356-139366; 2020.
P. Gomez, A. M. Kist, P. Schlegel, D. A. Berry, D. K. Chhetri, S. Dürr, M. Echternach, A. M. Johnson, S. Kniesburges, M. Kunduk, Y. Maryn, A. Schützenberger, M. Verguts, M. Döllinger. BAGLS, a multihospital benchmark for automatic glottis segmentation. Scientific Data, 7(1):186; 2020.
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