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Offer 3 (ResilEyes / LISSI): R&D Data Science Master internship

7 Octobre 2021


Catégorie : Stagiaire


R&D Data Science Master internship

 

Context

Post-traumatic stress disorder (PTSD) occurs in 5–10% of the population and is twice as common in women as in men. Although trauma exposure is the precipitating event for PTSD to develop, biological and psychosocial risk factors are increasingly viewed as predictors of symptom onset, severity and chronicity [1]. Post-traumatic stress disorder symptoms may start within one month of a traumatic event, but sometimes symptoms may not appear until years after the event [2,3]. These symptoms cause significant problems in social or work situations and in relationships. They can also interfere with your ability to go about your normal daily tasks. PTSD symptoms are generally grouped into four types: intrusive memories, avoidance, negative changes in thinking and mood, and changes in physical and emotional reactions. Symptoms can vary over time or vary from person to person.

There have been numerous studies of post-traumatic stress disorder in trauma victims, war

veterans, and residents of communities exposed to disaster [4-9].

In recent years, mental health disorders diagnosis systems have gained more attention from the machine learning community, with the main goal of assisting the clinicians for a better and more accurate diagnosis and prognosis. Please refer to our previous publications on AI for Mental Health and for psychiatry (https://www.researchgate.net/profile/Alice-Othmani).

ResilEyes is a French Deeptech startup developing AI solutions for mental health and PTSD detection, aid to diagnosis, follow-up, assistance and therapy (https://www.resileyes.com/).

In this internship, we are interested in developing a deep learning-based approach for PTSD diagnosis and continuous monitoring.

 

OUR INTERNSHIP PROGRAM/Tasks

We are seeking***TWO ***bright and highly motivated master students, who can work in the field of artificial intelligence. The project will develop innovative deep learning approaches for computer-aided diagnosis tools for Post-traumatic stress disorder (PTSD) recognition and follow-up. Innovative deep learning-based approaches will be developed. More details about the project will be given during the interview for confidentiality reasons.

The selected candidate will have the chance to work in an interdisciplinary team. This internship can lead to a permanent or a PhD scholarship.

ELIGIBILITY CRITERIA

  • The candidate must be an M2 Master student or in 5th year of an engineering school.
  • Has done M1 in computer science, applied mathematics or electrical engineering, with a focus on machine learning.
  • Experience in Deep learning and data analysis.
  • Experience in signal and image processing.
  • Demonstrated record of high-performance programming skills in python.
  • Demonstrated analytical, verbal, and scientific writing skills in English.

 

DURATION

Internship duration will be 6 months starting from January 2022 at an early date to start. The latest date to start the internship will be March/April 2022.

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 + transcripts + cover letter + recommendation letters to Alice.othmani@u-pec.fr and yannick.trescos@resileyes.com(before October 30, 2021).

 

When submitting

  • Thanks for mentioning “Master Internship candidature Offer 4: Resileyes” in the object of your mail
  • If you are interested and applying to several offers with Dr. Alice OTHMANI, precise your order of preference in the text of your mail and in the object for example “Master Internship candidature Offer 1: MSME, offer2: POWDER, Offer3: Resileyes”.

 

REFERENCES

 

[1] Yehuda, R., Hoge, C. W., McFarlane, A. C., Vermetten, E., Lanius, R. A., Nievergelt, C. M., ... & Hyman, S. E. (2015). Post-traumatic stress disorder. Nature Reviews Disease Primers, 1(1), 1-22.

[2] Chantarujikapong, S. I., Scherrer, J. F., Xian, H., Eisen, S. A., Lyons, M. J., Goldberg, J., ... & True, W. R. (2001). A twin study of generalized anxiety disorder symptoms, panic disorder symptoms and post-traumatic stress disorder in men. Psychiatry research, 103(2-3), 133-145.

[3] Bienvenu, O. J., Gellar, J., Althouse, B. M., Colantuoni, E., Sricharoenchai, T., Mendez-Tellez, P. A., ... & Needham, D. M. (2013). Post-traumatic stress disorder symptoms after acute lung injury: a 2-year prospective longitudinal study. Psychological medicine, 43(12), 2657-2671.

[4] Zhang, J., Richardson, J. D., & Dunkley, B. T. (2020). Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning. Scientific reports, 10(1), 1-10.

[5] McDonald, A. D., Sasangohar, F., Jatav, A., & Rao, A. H. (2019). Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: a supervised machine learning approach. IISE Transactions on Healthcare Systems Engineering, 9(3), 201-211.

[6] Leightley, D., Williamson, V., Darby, J., & Fear, N. T. (2019). Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. Journal of Mental Health, 28(1), 34-41.

[7] Galatzer-Levy, I. R., Ma, S., Statnikov, A., Yehuda, R., & Shalev, A. Y. (2017). Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Translational psychiatry, 7(3), e1070-e1070.

[8] Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2020). Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychological Medicine, 1-11.

[9] Banerjee, D., Islam, K., Xue, K., Mei, G., Xiao, L., Zhang, G., ... & Li, J. (2019). A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowledge and Information Systems, 60(3), 1693-1724.