PhD Candidate in Machine Learning Approaches to Conservation Condition Needs of Historic Buildings Norwegian University of Science and Technology in Norway

The Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU) invites applications for a PhD Candidate position focused on Machine Learning Approaches to Conservation Condition Needs of Historic Buildings. This opportunity is part of the Marie Sklodowska-Curie Action (MSCA) CHARM Doctoral Network, which aims to develop sustainable conservation and restoration solutions for heritage architecture, buildings, and sites, particularly in the context of hydro-climate factors and climate change.

NTNU is a leading technical-scientific university headquartered in Trondheim, Norway, with a strong commitment to professional education and research. The CHARM project brings together academic centers, cultural heritage organizations, SMEs, industrial producers, and NGOs from 9 European and 1 South American countries, offering a multidisciplinary and international research environment.

The doctoral project, titled "Understanding conservation condition and rehabilitation needs at district scale in time of energy saving and climate change via machine learning driven method," aims to implement artificial intelligence (AI) techniques, specifically convolutional neural networks (CNN), to automatically detect cracks and morphological indices from Synchrotron and SEM images. The project will develop deep learning neural networks to classify building stock at district scale, identify those more prone to decay based on construction year, location, materials, exposure, and use, and predict maintenance and restoration priorities using energy retrofit directives and risk maps.

Expected outcomes include building high-quality datasets to highlight facades prone to decay, applying deep learning for semi-automatic decay pattern segmentation and mapping, correlating maintenance needs with decay features at district/city level, and optimizing maintenance activities through machine learning insights. The research will contribute to the development of sustainable conservation solutions with low environmental footprint and high societal impact.

The supervisory committee consists of Full Professor Chiara Bertolin (NTNU), Associate Professor Chao Gao (NTNU), and Dr. Marie Louise Anker (Nidaros Cathedral Restoration Works). Planned secondments include research stays at FORTH Foundation for Research and Technology – Hellas (Crete, Greece) and CY Cergy Paris University (France), providing additional training in AI, ML algorithms, and materials science.

Duties include participation in the mandatory PhD research education programme, development and validation of deep-learning-based frameworks for micro-crack detection, definition and validation of micro-scale indices, classification of micro-morphologies and damages, participation in international research and dissemination activities, condition monitoring campaigns, and completion of a PhD thesis. The position offers opportunities for publishing in peer-reviewed journals and attending international conferences.

Applicants must hold a relevant Master's degree (Computer Science, Physics, Mathematics, Materials Engineering, Mechanical Engineering, Computer Engineering or equivalent) corresponding to a five-year Norwegian course with 120 credits at master's level. Master students may apply if the degree is completed before starting. A strong academic background (grade B or better), excellent English skills, experience with deep learning neural networks for image segmentation, and programming proficiency in Python and Matlab are required. Preferred qualifications include experience in processing-imaging techniques, condition monitoring, and analytical methods/models. Personal qualities such as motivation, independence, teamwork, and communication skills are valued.

The position is fully funded with a gross salary of NOK 550,800 per annum for three years, with a 2% statutory contribution to the State Pension Fund. Funding is provided by the Marie Sklodowska-Curie Action (MSCA) for the CHARM project. Additional benefits include career guidance, international research network, mentor programme, Norwegian language training, and favorable terms as a member of the Norwegian Public Service Pension Fund.

Applications must be submitted electronically via Jobbnorge.no by 29 June 2026. Required documents include transcripts, diplomas, CV, master's thesis, motivation letter, and names of three referees. All documentation must be in English. If invited for interview, certified copies of certificates and diplomas must be provided. NTNU values diversity and encourages applications from candidates of all backgrounds.

For further information, contact Professor Chiara Bertolin at [email protected]. The position is based in Trondheim, a vibrant city known for its rich cultural scene, excellent welfare system, and opportunities for education and family life.