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offer 2 (Lissi / Malaya University): R&D Data Science Master internship

5 Octobre 2021

Catégorie : Stagiaire

R&D Data Science Master internship




The Centre for Disease Control and Prevention, United States released a statistic (2020), on which they have identified 1 in 54 children as having autism spectrum disorder (ASD and is identified to be more common than childhood cancer [1]. Realizing the severity of this issue, the Malaysian government has responded to this global health issue by investing RM26 million [2] on treatment and education of ASD children. ASD is defined as a neurodevelopmental disorder that is perceived by a lack of social interaction and emotional intelligence, repetitive, abhorrent, stigmatized, and fixated behaviour [3]. Autism is not a disease and hence it needs no cure but requires longitudinal monitoring and assistance from the aspect of growth development [4]. In typical cases, intervention plans for ASD affected children are often designed based on the level of ASD severity [5].

This project is in collaboration between the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) at UPEC university (France) and the department of Artificial Intelligence at University of Malaya (Malaysia). Our goal in this project is to develop an Artificial Intelligence based platform for longitudinal monitoring and follow up of ASD children based on an automated approach for ASD’s severity level assessment.

Please refer to our previous publications on AI for Mental Health and for psychiatry (



We are seeking 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 ASD children severity levels assessment. An innovative deep learning-based approach will be proposed. 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 PhD scholarship.


  • 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.



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 2022.

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



Please send your CV + transcripts + cover letter + recommendation letters to and (before October 30, 2021).


When submitting

  • Thanks for mentioning “Master Internship candidature Offer 3: Malaysia” 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: Malaysia”.



1.Data & statistics on autism spectrum disorder. (2020, September 25). Retrieved April 04, 2021, from

2.Arumugam, T. (2016, August 05). Ukraine first Lady tours Permata KURNIA CENTRE: New Straits Times. Retrieved April 04, 2021, from

3.Gaigg, S. B. (2012). The interplay between emotion and cognition in autism spectrum disorder: Implications for developmental theory. Frontiers in Integrative Neuroscience, 6. doi:10.3389/fnint.2012.00113

4.Jaliaawala, M. S., & Khan, R. A. (2019). Can autism be catered with artificial intelligence-assisted intervention technology? A comprehensive survey. Artificial Intelligence Review, 53(2), 1039-1069. doi:10.1007/s10462-019-09686-8

5.Zachor, D. A., & Ben Itzchak, E. (2010). Treatment approach, autism severity and intervention outcomes in young children. Research in Autism Spectrum Disorders, 4(3), 425-432. doi:10.1016/j.rasd.2009.10.013

6.S. S. Rajagopalan, A. Dhall and R. Goecke, "Self-Stimulatory Behaviours in the Wild for Autism Diagnosis," 2013 IEEE International Conference on Computer Vision Workshops, Sydney, NSW, Australia, 2013, pp. 755-761, doi: 10.1109/ICCVW.2013.103.

7.Liang, S., Sabri, A. Q., Alnajjar, F., & Loo, C. K. (2021). Autism spectrum self-stimulatory behaviors classification using explainable temporal coherency deep features and svm classifier. IEEE Access, 9, 34264-34275. doi:10.1109/access.2021.3061455