VAC-2025-65 – PhD position on AI-driven Structural Health Monitoring and Safety Assessment of D[...]

cimne; Barcelona; Presencial; EUR 20.000 - 30.000; Jornada completa

Descripción de la vacante

A leading research institution in Barcelona seeks a candidate for a doctoral thesis on dam safety and predictive maintenance using AI. Responsibilities include developing methods for data integration and evaluating AI approaches. Candidates should have a Master’s in Civil Engineering or related fields and strong programming skills, particularly in Python. This role offers opportunities for interdisciplinary research and collaboration with international partners.

Formación

• Eligibility for enrolment in a PhD programme.
• Strong programming skills are essential.
• Background in numerical modelling and machine learning is required.

Responsabilidades

• Generate a knowledge base for dam safety.
• Evaluate AI approaches for decision support.
• Develop methods for integrating heterogeneous data.
• Collaborate on the design of a prototype AI-agent.
• Publish scientific results in international journals.

Conocimientos

Programming (Python)

Numerical modelling (FEM)

Machine learning / AI

Good communication skills in English

Educación

Master’s degree in relevant field

Herramientas

ML frameworks (TensorFlow, PyTorch, Scikit-learn)

Descripción del empleo

Overview

Functions to be developed :

A doctoral thesis in the framework of the research project entitled DAMSHAI : Dam Structural Health Monitoring and Safety Assessment with an AI-driven framework, with reference Project PID2024-157828OB-I00 funded by MCIN / AEI / 9 / 501100011033 and by FEDER, EU, funded by the Spanish Proyectos de Generación de Conocimiento 2024. Principal investigator : Dr. Fernando Salazar ().

The DAMSHAI project explores the potential of AI agents to support decision-making in dam safety and predictive maintenance, integrating numerical modelling (FEM), monitoring data, machine learning (ML) predictions, and expert knowledge.

Tasks to be performed :

Responsibilities

• Contribute to the generation of a knowledge base, integrating monitoring data, FEM models, technical reports, and expert surveys.
• Participate in the evaluation of AI approaches (prompt engineering, fine-tuning of LLMs, and rule-based expert systems) for decision support in dam engineering.
• Develop methods for integrating heterogeneous data (numerical, categorical, textual) into an AI-agent for anomaly detection and safety assessment.
• Collaborate in the design, implementation, and validation of a prototype AI-agent applied to a real case study of dam behavior.
• Prepare and publish scientific results in leading international journals and conferences.

Collaborations

Additional information about the project is available at : CIMNE RTD Project :

The candidate will join the Machine Learning in Civil Engineering group at CIMNE Madrid, in collaboration with UPC and international partners (Portugal, USA, Ecuador).

This contract is financed by the announcement of Proyectos de Generación de Conocimiento 2024 of the Ministerio de Ciencia, Innovación y universidades : Proyectos de Generación de conocimiento 2024| Agencia Estatal de Investigación ().

Required skills

• Master’s degree (or equivalent) in Civil Engineering, Structural Engineering, Computational Mechanics, Computer / Data Science, or related fields; eligible for enrolment in a PhD programme.
• Solid programming skills (Python preferred).
• Background in numerical modelling (FEM) and machine learning / AI.
• Good written and oral communication skills in English.

Other valued skills (not mandatory)

• Knowledge of dam engineering or structural safety.
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
• Experience in high-performance computing environments.
• Analytical skills, autonomy, and motivation for interdisciplinary research.
• Spanish or Catalan language skills.

Qualification system

The evaluation process must comply with the following criteria and sub-criteria :

• Criterion Academic and / or scientific-technical track record of the candidate (up to 50 points). Scientific-technical contributions (up to 45 points). The candidate’s academic record and other curricular merits will be assessed, as well as their relevance to the tasks to be carried out, considering the candidate’s training and professional experience. Mobility and internationalization (up to 5 points). The relevance and impact on the candidate’s research career of stays in national and international centers and / or in the industrial sector will be assessed, taking into account the prestige of the host institution and the activities carried out there.
• Criterion 2. Suitability of the candidate for the research activities to be carried out (up to 50 points). The candidate’s suitability for the program, project, or research activities to be carried out will be assessed based on their prior training and experience. Consideration will be given to the added value that undertaking the project will represent for the candidate’s research career, as well as the value contributed to the host center and team.