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Annonce

28 mars 2022

Research Fellow (postdoctoral) positions in deep learning for mental disorders diagnosis


Catégorie : Post-doctorant


Research Fellow (postdoctoral) positions in deep learning for mental disorders diagnosis

Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood. There are many different conditions that are recognized as mental illnesses. Mental disorders or illnesses include: depression, bipolar disorder, schizophrenia and other psychoses, dementia, and developmental disorders including autism. There are effective strategies for preventing mental disorders such as depression.

In Prof. Alice OTHMANI’s Team, we are interested in developing artificial intelligence based solutions for mental health diagnosis, prognosis, follow-up and drug discovery.

We are opening several postdoc positions starting from September 2022 to September 2023.

 

General requirements:

 

Specific technical requirements:

 

DURATION

1 to 3 years starting from September 2022 at an early date to start.

Location: Université Paris-Est Créteil, Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), 122 rue Paul Armangot, 94400 Vitry sur Seine

 

APPLICATION

Please send your CV + cover letter + list of publications + recommendation letters to Alice.othmani@u-pec.fr (before June, 2022).

N.B. Only shortlisted applicants will be notified + Salary is very competitive + this postdoc position can lead to permanent academic position.

 

Few of our publications related to the postdoc topic:

-Rejaibi, E., Komaty, A., Meriaudeau, F., Agrebi, S., & Othmani, A. (2022). MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. Biomedical Signal Processing and Control, 71, 103107.

-Muzammel, M., Salam, H., & Othmani, A. (2021). End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis. Computer Methods and Programs in Biomedicine, 211, 106433.

-Muzammel, M., Othmani, A., Mukherjee, H., & Salam, H. (2021, June). Identification of signs of depression relapse using audio-visual cues: A preliminary study. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 62-67). IEEE.

-Yasin, S., Hussain, S. A., Aslan, S., Raza, I., Muzammel, M., & Othmani, A. (2021). EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review. Computer Methods and Programs in Biomedicine, 202, 106007.

-Muzammel, M., Salam, H., Hoffmann, Y., Chetouani, M., & Othmani, A. (2020). AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis. Machine Learning with Applications, 2, 100005.

 

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