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Enrique García Macías

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PhD Opportunity in Intelligent Structural Health Monitoring, Artificial Intelligence and Digital Twins at the University of Granada University of Granada in Spain

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Nov 12, 2026

Country flag

Country

Spain

University

University of Granada

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Keywords

Computer Science
Artificial Intelligence
Civil Engineering
Structural Engineering
Architecture
Digital Twin Technology

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About this position

The University of Granada is seeking a highly motivated PhD candidate to join IMIS-Lab in Spain. The project focuses on intelligent structural health monitoring and the assessment of infrastructure through advanced digital technologies.

The research combines artificial intelligence, digital twins, and structural monitoring to support improved understanding and evaluation of infrastructure. The work is suited to candidates interested in applying computational and data-driven approaches to structural engineering and civil engineering challenges.

IMIS-Lab provides the research setting for this interdisciplinary PhD opportunity, bringing together structural mechanics, hydraulic engineering, infrastructure assessment, and intelligent systems. The opening is associated with the FPU 2026 call at the University of Granada and is supervised by Associate Professor Enrique García Macías.

Funding details

Available

What's required

Applicants should have a background in Civil Engineering, Structural Engineering, Architecture, Computer Science, or a related field, and a GPA above 8.5/10 or equivalent for international degrees, subject to the official FPU academic assessment.

How to apply

Send your CV and academic transcript to [email protected] as soon as possible. Review the FPU 2026 information and the international GPA equivalence resource using the provided links.

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Professors

Associate Professor

University Name
Country flag

Spain

Research Interests

Artificial Intelligence

Structural Engineering

Computer Science

Civil Engineering

Digital Twin Technology