Hybrid Artificial Intelligence and Statistical Computational Modeling for Early Diagnosis of Brain Disorders

This doctoral research project aims to investigate the statistical modeling of Alzheimer’s disease (AD) progression. The research will combine advanced statistical methods, joint modeling approaches, as well as machine learning and deep learning techniques to jointly analyze longitudinal biomarkers and time-to-event outcomes, enabling a comprehensive representation of disease dynamics.

The project will focus on developing robust, interpretable, and clinically relevant models capable of capturing disease progression while addressing common challenges such as missing data, irregular follow-up, and patient heterogeneity. The research will rely on real-world datasets (e.g., ADNI or similar cohorts) and may integrate classical statistical approaches with modern machine learning techniques when appropriate.

The expected outcomes include methodological contributions to joint modeling, improved risk prediction tools for AD progression, and publications in peer-reviewed international journals.

Research Environment

Candidates are expected to carry out their research full-time within the structures of the Euromed University of Fes.

PhD Student’s Responsibilities

• Conduct a comprehensive review of the literature on Alzheimer’s disease progression, survival analysis, and joint modeling approaches.
• Develop and implement models for longitudinal and time-to-event data.
• Perform data preprocessing, including missing data handling and longitudinal data structuring.
• Apply the proposed methods to real clinical and biomedical datasets related to Alzheimer’s disease.
• Evaluate model performance using appropriate predictive metrics.
• Prepare scientific manuscripts for submission to international peer-reviewed journals.

Candidate Profile

• Holder of an Engineering degree or Master’s degree or equivalent in Statistics, Applied Mathematics, Data Science, Computer Science, Biomedical or related scientific discipline.
• Strong background in statistical modeling, with basic knowledge of Machine Learning and Deep Learning.
• Proficiency in programming in R and/or Python.
• Demonstrated interest in health, biomedical, or clinical research is considered an asset.
• Excellent command of English, both written and spoken.

The application file must include the following documents:

• CV with photo
• A cover letter
• A copy of diplomas
• A copy of academic transcripts
• A copy of CIN/Passport

Submission of the Application File

The application file must be sent to the Doctoral Studies Center (CEDoc) of the Euro-Mediterranean University of Fes by email no later than February 14, 2026, to the following email address:

Euromed-CEDoc@ueuromed.org

For More Information

Administrative Affairs Officer of the CEDoc:
Ms. Boutaina Jai Mansouri – b.jai-mansouri@emadu.ueuromed.org

Director of Research and of the CEDoc:
Prof. Abdelghafour Marfak – a.marfak@euromed.org

Thesis Supervision

Thesis Supervisor:
Pr. Abderazzak Mouiha – a.mouiha@ueuromed.org