Physics-informed neural networks (PINNs) for biological and health applications

The PhD project focuses on the development and application of Physics-Informed Neural Networks (PINNs) to model complex biological and physiological systems. PINNs combine data-driven machine learning with mechanistic laws expressed as differential equations, enabling robust modeling even in data-scarce or noisy biomedical contexts.

The research will explore how PINNs can be used to improve predictive modeling, parameter estimation, and uncertainty quantification in health-related problems such as tissue mechanics, cardiovascular dynamics, biomedical imaging, disease progression, or drug delivery. The project aims to bridge the gap between computational biology, applied mathematics, and artificial intelligence, with potential impact on personalized medicine and clinical decision support.

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

The PhD student will be expected to:

• Conduct original research on PINN methodologies for biological and health applications
• Develop mathematical models based on physical, physiological, or biological laws
• Design, implement, and validate PINN architectures using modern ML frameworks
• Analyze experimental or clinical data and integrate them with physics-based models
• Compare PINNs with traditional numerical and data-driven approaches
• Publish research outcomes in high-impact peer-reviewed journals, with the objective of producing at least four journal articles, including a minimum of two Q1 publications
• Actively participate in group meetings, seminars, and collaborative projects
• Contribute to teaching or supervision activities, depending on institutional requirements

Candidate Profile

The ideal candidate should have:

• A Master’s degree (or equivalent) in Applied Mathematics, Computer Science, AI, or a related field
• Solid background in partial differential equations, numerical methods, or scientific computing
• Strong interest in machine learning and deep learning, particularly for scientific applications
• Programming skills in Python (experience with PyTorch/TensorFlow is an advantage)
• Interest in biological, biomedical, or health-related modeling problems
• Ability to work independently and in interdisciplinary teams
• Good written and oral communication skills in English

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 June 19, 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. MASROUR Mohammed – m.masrour@ueuromed.org