招聘岗位：生物医学图像方向博士后

Recruitment position: Postdoctoral Researcher of Biomedical Imaging

Please submit your english CV to ahmad8chaddad@gmail.com

Kindly visit our research website: https://sites.google.com/view/ahmad-chaddad/aipm-lab

Ahmad Chaddad received the Ph.D. degree in engineering systems from the University of Lorraine, Metz, France, in 2012. He worked for seven years at McGill University and école de Technologie Supérieure (ETS), Montreal, QC, Canada, The University of Texas MD Anderson Cancer Centre, Houston, TX, USA, and Villanova University, Villanova, PA, USA. He is a Master and PhD Supervisor. In 2020, he joined the School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China, as a Professor. He is an Associate Member at the Laboratory for Imagery, Vision and Artificial Intelligence, ETS. He has authored more than +120 research papers in SCIE (He is the first author for +85 papers), 18 abstracts and 2 technical reports with Scholar H-Index= +32. He leads +11 projects (i.e., China <1 NSFC, 2 Talent foreigner program, 2 Guangxi province, 1 Guilin city, and 2 GUET> and Canada <Nova Scotia fund>). Prof. Chaddad is an associated Editor in Frontier in Nuclear Medicine and Guest Editor in Applied Sciences journal. He was a chair for three special issues in international conferences (IEEE IPTA2017, CIE45 2015, IEEE CODIT2014), and serve as a member/reviewer of several international technical and organizational committees (i.e., AAAI, ISBI, MICCAI, CVPR). His current research interests include AI and radiomics analysis to improve personalized medicine strategies, by allowing clinicians to monitor disease in real time as patients move through treatment.

 

2023/01-2026/12 

    NSFC: Regional Science Foundation Project (National grant) 

    PI: Chaddad A. Collaborators: Ismail Ben Ayed, Christian Desrosiers and Ahmed Bouridane 

    Title: An interpretable deep radiomics model for personalized treatment of brain tumors 

    Approved budget: 330.000 RMB 

2023/05-2026/05 

    Guangxi Science and Technology Base and Talent Project: The introduction of high-level talents at home and abroad 

    PI: Chaddad A. 

    Title: AI-based personalized treatment of COVID-19 patients. 

    Approved budget: 190.000 RMB 

2023/06-2025/06 

    Guilin Innovation Platform and Talent Program (C26) 

    PI: Chaddad A. 

    Title: AI-based prediction of brain tumor treatment 

    Approved budget: 600.000 RMB 

2023/05-2026/05 

    Guangxi Science and Technology Base and Talent Project: Young scientific and technological innovation talents

    PI: Chaddad A. 

    Title: Radiomics analysis of brain tumors 

    Approved budget: 200.000 RMB 

2021/01-2022/12 

    Foreign Young Talents Program (National grant) 

    PI: Chaddad A. 

    Title: Radiomics analysis for predicting Autism Spectrum Disorder. 

    Requested budget: 300.000 RMB 

    Status: approved

2020/01-2021/12 

    Foreign Young Talents Program (National grant) 

    PI: Chaddad A. 

    Title: Deep radiomic models for the personalized management of prostate cancer. 

    Requested budget: 150.000 RMB 

    Status: approved 

2021-2023 

    Research Nova Scotia; ACURA 

    PI: Kucharczyk MJ. 

    Co-Investigators: Chaddad A. , Clarke S., Rendon R., Beyea S., Mason R., Bowen C. & Matheson K. 

    Title: Can Magnetic Resonance Imaging of the Prostate combined with a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (Can MRI-PREDICT). 

    Project ID: RNS-NHIG-2020-1384 

    Requested budget: $97.680 CAD. 

    Status: approved 

2021-2024

    Guilin University of Electronic Technology 

    PI: Chaddad A. 

    Title: Artificial intelligence for personalized medicine. 

    Requested budget: 200.000 RMB. 

    Status: approved 

    Guilin University of Electronic Technology 

    PI: Chaddad A. 

    Research platform services 

    Requested budget: 300.000 RMB. 

    Status: approved 

 

Example of our team papers:

1. Y. Jiang, X. Zhao, Y. Wu, and Chaddad, Ahmad, “A knowledge distillation-based approach to enhance transparency of classifier models,” in 2025 Thirty-Ninth AAAI Conference on Artificial Intelligence, AAAI, 2025, pp. 1–9.

2. Y. Wu, C. Desrosiers, and Chaddad, Ahmad, “Facmic: Federated adaptative clip model for medical image classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 531–541.

3. Wu, Y., Chaddad, A., Desrosiers, C., Daqqaq, T., & Kateb, R. (2025). "FAA-CLIP: Federated adversarial adaptation of CLIP." IEEE Internet of Things Journal, accepted (February 22, 2025).

4. Chaddad, A., Jiang, Y., Daqqaq, T., & Kateb, R. (2025). "EAMAPG: Explainable adversarial model analysis via projected gradient descent." Computers in Biology and Medicine, 188, 109788.

5. Chaddad, A., Wu, Y., Jiang, Y., Bouridane, A., & Desrosiers, C. (2025). "Simulations of common unsupervised domain adaptation algorithms for image classification." IEEE Transactions on Instrumentation & Measurement, 74, 1–17.

6. Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2022). "Explainable, domain-adaptive, and federated artificial intelligence in medicine." IEEE/CAA Journal of Automatica Sinica, 10(4). https://doi.org/10.1109/JAS.2023.123123 

7. Chaddad, A., Hassan, L., & Desrosiers, C. (2022). "Deep radiomic analysis for predicting coronavirus disease 2019 in computerized tomography and X-ray images." IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3119071 

8. Chaddad, A., & Sargos, P. (2021). "Modeling texture in deep 3D CNN for survival analysis." IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2020.3025901 [Impact Factor = 7.7].

9. Chaddad, A., & Daniel, P. (2019). "Novel radiomic features based on joint intensity matrices for predicting glioblastoma patient survival time." IEEE Journal of Biomedical and Health Informatics, 23, 795–804. https://doi.org/10.1109/JBHI.2018.2825027 [Impact Factor = 7.7].

10. Chaddad, A., & Liang, X. (2024). "Stability of radiomic models and strategies to enhance reproducibility." IEEE Transactions on Radiation and Plasma Medical Sciences. https://doi.org/10.1109/TRPMS.2024.3365778.

11. Chaddad, A., Wu, Y., & Desrosiers, C. (2023). "Federated learning for healthcare applications." IEEE Internet of Things Journal, accepted (October 16, 2023).

12. Chaddad, A., Hassan, L., & Katib, Y. (2023). "A texture-based method for predicting molecular markers and survival outcome in lower grade glioma." Applied Intelligence. https://doi.org/10.1007/s10489-023-04844-6.

13. Rathore, S., Iftikhar, M., Chaddad, A., Singh, A., Gillani, Z., & Abdulkadir, A. (2023). "Imaging phenotypes predict overall survival in glioma more accurately than basic demographic and cell mutation profiles." Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2023.107812

14. Chaddad, A., Daniel, P., Zhang, M., Rathore, S., Sargos, P., Desrosiers, C., & Niazi, T. (2022). "Deep radiomic signature with immune cell markers predicts the survival of glioma patients." Neurocomputing. https://doi.org/10.1016/j.neucom.2020.10.117.

More details about the research papers are presented at https://scholar.google.com/citations?hl=en&user=ACaSfwoAAAAJ&view_op=list_works&sortby=pubdate

 

1. Digital signal processing (数字信号处理) + MATLAB

2. Digital image processing (数字图像处理) + MATLAB

3. Digital system design (数字系统与设计) + VHDL (ModelSIM)

4. Computer Vision (计算机视觉) + MATLAB/PYTHON

5. Deep learning (深度学习) + MATLAB/PYTHON