The PhD will develop methods and digital tools for analyzing and optimizing building energy autonomy and human well-being. The research will combine parametric design, building-performance simulation, energy-balance analysis, and well-being assessment within regenerative design workflows. The resulting multi-criteria methods will be converted into surrogate-model-based digital tools for rapid combined analysis. The work will contribute to the REGEN4BE Horizon Europe project.
Research questions and objectives:
State of the art and research gaps
Current building-design methods generally address energy-demand reduction and on-site generation separately and are rarely integrated early enough to influence fundamental design decisions. Well-being is commonly reduced to standardized comfort thresholds, while broader criteria are rarely considered. Designers consequently lack practical methods for exploring trade-offs between energy autonomy and human well-being. Detailed simulations and multi-criteria exploration are also computationally demanding, limiting rapid design exploration.
Objective 1 — Leverage parametric design for adaptive building configurations by developing workflows that connect building form and passive strategies, use patterns, current and future climate conditions, and dynamic energy-balance calculations. The algorithms will coordinate energy-demand reduction with on-site BIPV generation, storage, and grid integration.
Objective 2 — Develop multi-criteria analysis methods combining energy autonomy with quantitative and qualitative human well-being indicators. Datasets describing parametric building variables, energy performance, well-being KPIs, and the trade-offs characterizing optimal solutions will be generated and analyzed.
Objective 3 — Use the generated datasets to develop AI-based surrogate models for rapid prediction of combined energy and well-being performance. The resulting models will be implemented in digital tools to enable real-time comparison of alternative design scenarios.
Responsibilities and (foreseen) tasks:
Review regenerative design approaches, including their targets and performance indicators.
Review research on building energy balance, human well-being, and AI-based surrogate modeling.
Develop parametric simulation workflows integrating passive design, energy demand, BIPV, storage, and grid integration.
Develop energy-balance algorithms and identify solutions that maximize building energy autonomy.
Translate well-being criteria from building standards and research into computable indicators.
Generate simulation datasets and develop, train, and validate AI-based surrogate models.
Collaborate with REGEN4BE partners in programming and integrating the resulting methods into digital design tools for energy autonomy and human well-being.
Applicants should fulfil the following requirements:
Master's degree in architecture, civil engineering, building engineering, or a closely related field.
Clear interest in the topic of the position.
Excellent command of spoken and written English.
Strong and demonstrable writing and analytical skills.
Capacity to work both as an independent researcher and as part of an international team.
Capacity and willingness to assist with organizational tasks relevant to the project.
The following experience is beneficial:
Knowledge of building energy performance, including passive design, energy demand, renewable generation, and storage.
Experience in performing building-performance simulations with EnergyPlus.
Proficiency with parametric environmental workflows (e.g., Rhino/Grasshopper, Ladybug Tools and ClimateStudio).
Programming skills (e.g., Python or C#) or a strong capacity to acquire these skills.
Competence in statistical analysis and machine learning, or a clear willingness to acquire these skills.
Knowledge of indoor environmental quality, occupant comfort, and well-being standards.
We offer:
4-year PhD position in one of the largest, most internationalized and leading civil engineering and architecture research centers in Estonia.
The chance to do high-level research in one of the most dynamic research contexts globally.
Opportunities for conference visits and networking with globally leading universities and research centers in the fields of built environment sustainability, digitalization and energy efficiency.
The PhD is funded through the EU Horizon Europe program with a minimum gross salary of 2300 EUR/month.
The PhD project duration will be 4 years from January 2027 to December 2030.
Supervisors:
Main supervisor: Senior Researcher Francesco De Luca, School of Engineering: Department of Civil Engineering and Architecture: Academy of Architecture and Urban Studies
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 Department of Civil Engineering and Architecture of TalTech – Tallinn University of Technology brings together seven research groups engaged in teaching and research in the fields of architecture, building structures, construction processes, near zero energy buildings, structural and fluid mechanics, road construction, geodesy and water and environmental engineering. The affiliation of the PhD candidate will be the Academy of Architecture and Urban Studies of the Department of Civil Engineering and Architecture.
For information about the admission process, please visit the PhD Admission homepage
Applications can be submitted from 16.09.2026 to 04.10.2026
For further info, please contact:
Main supervisor Francesco De Luca at francesco.deluca@taltech.ee or the doctoral admission team at docstudy@taltech.ee
