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

Alice Othmani alice.othmani at u-pec.fr
Mon Mar 28 14:23:50 -04 2022

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:

  *   Self-motivated scientist/Ph. D graduate to pursue a scientific career. Independent and passionate about medical data processing projects;

  *   Good team player. Able to undertake independent research projects under the direction of the PI together with other team members;

  *   Hold a Ph.D. in a relevant field of computer vision, image processing or machine vision, or other relevant fields;

  *   Excellent scientific/technical writing skills and communication capability;

  *   Prior advanced experience in working with data analysis projects.

Specific technical requirements:

  *   Excellent experience, knowledge, and skills in programming languages, particularly python;

  *   Excellent knowledge and skills in digital image and signal processing + previous Video analysis experience is a big plus for acceptance

  *   Deep understanding of AI learning, machine learning and data science with hand-on skill and experience. Advanced understanding and experiences on deep learning

  *   Skills for numerical computational algorithms; Strong in mathematics theories.

  *   Rigid and logical thinking of scientific problems;

  *   High ranked publications in Q1 journals (IEEE transactions, Pattern recognition, Computer Vision and Image Understanding, ..) and conferences (MICCAI, IEEE CBMS, ISBI, CVPR, ICPR, ICIP) is mandatory


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


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

N.B. Only shortlisted applicants will be notified + 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.

Alice OTHMANI, Ph.D.
Associate professor / Maître de conférences
UPEC - Université Paris-Est Créteil (ex-Université Paris 12)
Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi)- EA-395
61 Avenue du Général de Gaulle, 94000 Créteil
122 rue Paul Armangot, 94400 Vitry sur Seine
Email : alice.othmani at u-pec.fr
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