The aim of this PhD project is to assess and support the monitoring of built environment and construction processes (new constructions, renovation, material reuse, etc.) across scales with AI-based workflows. Applicants should have a relevant master’s degree, programming skills, AI competence, knowledge of drone surveys and willingness and capabilities to carry out regular drone surveys.
Research description:
Drone survey is already well used in the built environment and construction sector. With the support of the survey, large volumes of data can be easily collected and rapidly collected. However, it is quite common that the data collection is carried out within a limited scope and most of the collected data becomes useless and simply archived from day one. With the era of AI, the data collection can be defined in a lot more purposeful through automated information extraction processes. Once information extraction processes are more dynamic, the use of drone survey can be more supportive for construction sector daily operations, and the extent of valuable use cases will increase. For example, combining non-destructive data collection, AI-based material recognition and building information modelling (BIM) enables municipalities, the construction sector, and designers to plan material reuse in advance, cut virgin material use, and significantly reduce waste. The position is embedded within the newly established Interreg project CIRCAIDE which creates an AI-supported digital service that helps bring together and use information about buildings and building materials before demolition or construction work begins.
Relevant questions which this PhD project could address:
How to build up a use case based and dynamic drone survey program?
How to apply AI-based workflows with drone survey programs?
How to apply advanced data analysis capabilities to extract information from drone surveys?
Applicants should fulfil the following requirements:
university degree (MSc) in civil engineering and/or construction management;
advanced computer literacy and programming skills;
advanced AI agent build-up competencies (chatGPT, Claude etc.);
previous knowledge of drone surveys (including drone operator tasks);
skills in data analysis, mathematics, and statistics;
ability for independent research as part of a team, interest in the presentation/publication of scientific results;
willingness and readiness for physically demanding work (ex. carrying out regular outdoor drone surveys);
good command of spoken and written English.
We offer:
supervision by experienced researchers in construction information, building information modelling/management;
a fully-funded fixed-term 4-year PhD Early-stage researcher position with a gross monthly salary of 2300 euros;
an internationally active and interdisciplinary research environment at the interface of building lifecycle research;
access to collaborative research networks;
support for conference participation, research visits, and international collaboration;
training in advanced modelling, reproducible computational workflows, scientific writing, and science communication.
Supervisors:
Main supervisor: Tenured Associate Professor Raido Puust, School of Engineering: Department of Civil Engineering and Architecture: Building Lifecycle Research Group
Tallinn University of Technology (TalTech) is an international scientific community with approximately 9,000 students and 2,000 employees; it is one of the largest universities in Estonia, the leading EU country in digitalisation. The university's strengths are broad multidisciplinary study/research interests, a modern research environment, and strong collaboration with international educational and research institutions. TalTech is aiming to be an organisation leading the way to a sustainable digital future.
The Building Lifecycle Research team at the Department of Civil Engineering and Architecture, TalTech, is an internationally active research group working in approaching s the building lifecycle as a whole, integrating the construction process and its outcomes with management strategies, technologies, building materials, economics and facilities management.
For information about the admission process, please visit the PhD Admission homepage
