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

cimne; Barcelona; Presencial; EUR 24.000 - 30.000; Jornada completa

Descripción de la vacante

A research institution is offering a doctoral thesis position focused on AI-driven dam safety and predictive maintenance. Applicants should have a Master’s degree in Civil Engineering or similar and solid programming skills. The role involves contributing to knowledge generation, evaluating AI methods, and publishing research. Collaboration with international teams will enhance the experience.

Formación

• Master’s degree (or equivalent) in a relevant field.
• Solid programming skills (Python preferred).
• Background in numerical modelling (FEM) and machine learning.

Responsabilidades

• Contribute to the generation of a knowledge base.
• Participate in the evaluation of AI approaches.
• Develop methods for integrating heterogeneous data.
• Collaborate in the design of a prototype AI-agent.
• Prepare and publish scientific results.

Conocimientos

Programming skills (Python preferred)

Background in numerical modelling (FEM)

Machine learning / AI

Good written and oral communication skills in English

Educación

Master’s degree in Civil Engineering or related fields

Herramientas

TensorFlow

PyTorch

Scikit-learn

Descripción del empleo

Overview

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 PID OB-I00 funded by MCIN / AEI / 9 / 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.

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