PRR Project

Summary

This position focuses on advancing research in Data Reliability and AI Modelling to support various engineering domains including Computational Hydraulics, Structural Engineering (applied to bridges, buildings, dams, and tunnels), Transportation, and Environment. The Auxiliary Researcher will be an integral part of a collaborative team and will be responsible for conducting the research to enhance the reliability, validity, and overall quality of data used in computational models and retrieved from the monitoring systems. The primary aim is to develop innovative methodologies and techniques to ensure the quality and trustworthiness of data sources, particularly in scenarios where data may be noisy, incomplete, or subject to various forms of uncertainty across the several intervention areas of LNEC.The successful candidate will be responsible for exploring cutting-edge techniques in data preprocessing, feature engineering and anomaly detection to improve data quality and reliability. This includes developing and implementing data validation and verification procedures to identify and mitigate potential errors or biases in data collection, storage, and processing pipelines. Additionally, the candidate will develop AI models that can adapt to dynamic and evolving data environments, incorporating mechanisms for robustness, fairness and transparency.The outcomes of the research will be applied to the various areas. For instance, in Computational Hydraulics and Environment, the candidate will work on enhancing the reliability of data collected from various sources such as river flow sensors, rainfall gauges, and hydraulic models used to simulate water-related climate changes. In Structural Engineering, the focus can also be on ensuring the reliability of structural health monitoring data from sensors installed on bridges, buildings, dams, and tunnels. The candidate will employ AI techniques to detect structural anomalies, predict maintenance needs, and optimize inspection schedules to ensure the safety and longevity of critical infrastructure. In a generic way, the candidate will address challenges related to data reliability in monitoring networks that can affect simulation models or related control systems.Collaboration with multidisciplinary teams will be essential, as the research conducted will intersect with various scientific areas and engineering disciplines. The candidate will have the opportunity to collaborate closely with researchers and practitioners in each engineering domain to address common challenges and develop innovative solutions. At the same time, he/she will be responsible for the development of different level training courses, from basic to advanced, in data science to promote the widespread use of the state of the art techniques and to promote the interconnections between the various researchers at LNEC.Furthermore, establishing robust partnerships and collaborations within the broader research community will be essential. The candidate will cultivate relationships with relevant stakeholders in industry and public institutions to identify emerging trends and challenges in data quality and AI applications. This collaborative approach will not only enhance the quality and relevance of research conducted but also foster knowledge exchange and facilitate the translation of research findings into practical applications. Actively seeking opportunities to participate in national and international research initiatives and consortia will also be encouraged to expand the reach and impact of the candidate’s research. Finally, the candidate is expected to disseminate research outcomes through presentations at conferences and publications in peer-reviewed journals to contribute to the advancement of knowledge in these areas.Candidates for this position should possess a strong background in data science, machine learning, or a related field, with expertise in monitoring data reliability, quality assurance, and AI modelling techniques. Proficiency in programming languages such as Python or MATLAB is essential, along with experience working with large-scale datasets (extended database knowledge and experience) and machine learning libraries/frameworks. Strong analytical and problem-solving skills are required, along with the ability to communicate complex concepts effectively to both technical and non-technical audiences. The quality of the past research in particular during Ph.D. stage as well as the publication record will be differentiating factors along with experience in working/developing research in the scope of multidisciplinary teams.This position offers a unique opportunity to contribute to cutting-edge research in monitoring data reliability, data quality assurance and AI modelling across multiple engineering domains within a dynamic and collaborative team environment, advancing scientific knowledge and developing practical solutions with real-world impact in several fields.