Mostafa El Sayed

Carleton University in Canada is advertising fully funded PhD opportunities in the Aerospace Structures and Materials Engineering Laboratory (ASME-Lab) within the Department of Mechanical and Aerospace Engineering . The positions are supervised by Prof. Mostafa El Sayed and are aimed at highly motivated, autonomous, and academically strong candidates. The lab works at the intersection of aerospace structures , advanced materials , multiphysics simulation , optimization , and data-driven engineering . Research directions include: Computational Fluid Dynamics (CFD) , Fluid–Structure Interaction (FSI) , and aeroacoustics ; data-driven modeling for engineering systems using machine learning, physics-informed machine learning, and reduced-order modeling; and fracture, fatigue, and structural health monitoring for 3D-printed metals, composites, and hybrid materials. Eligibility highlights include graduation from an internationally recognized university, a minimum academic standing equivalent to CGPA 10.5/12 on Carleton’s scale in the last 20 relevant courses, at least one first-author publication with the Master’s supervisor in a Q1 or Q2 journal , and strong analytical, computational, experimental, and/or programming skills. Interested applicants should email their CV , transcripts , and a brief statement of research interests to [email protected] . The post says that three PhD students will be recruited and that only shortlisted candidates will be contacted.

Fully funded PhD opportunities are available at Carleton University in the Department of Mechanical and Aerospace Engineering , within the Aerospace Structures and Materials Engineering Laboratory (ASME-Lab) . The positions are for students interested in aerospace structures, advanced materials, multiphysics simulation, optimization, and data-driven engineering. The research areas include Computational Fluid Dynamics (CFD) , Fluid-Structure Interaction (FSI) , aeroacoustics , structural modal analysis, wind tunnel testing, and numerical/experimental validation; machine learning , physics-informed machine learning, reduced-order modeling, and data-driven methods for engineering systems; and fracture mechanics , fatigue analysis, damage detection, and structural health monitoring for 3D-printed metals, composites, and hybrid materials. Applicants should be highly motivated, autonomous, and academically strong. Required qualifications include graduation from an internationally recognized university, a minimum academic standing equivalent to CGPA 10.5/12 on Carleton's scale in the last 20 relevant courses, at least one first-author publication with the Master's supervisor in a Q1 or Q2 journal , and strong analytical, computational, experimental, and/or programming skills. To apply, candidates should email their CV , transcripts , and a brief statement of research interests to Prof. Mostafa El Sayed at [email protected] . Only shortlisted candidates will be contacted.