PhD: Uncertainty-Driven Building Renovation Modelling

Delft; Netherlands; Remote; EUR 37,000 - 47,000; Full time

Benefits offered by this job

8% holiday allowance

End-of-year bonus 8.3%

Relocation assistance

Discounts on health insurance

Monthly work costs contribution

Job summary

Delft University of Technology is inviting applications for a PhD position on Uncertainty-based Life-Cycle Modelling for Building Renovation Decision-Making. The project is part of Horizon Europe CLIMABUILD and spans 48 months, with collaboration across research groups in the Faculty of Architecture and the Built Environment.

The successful candidate will develop probabilistic life-cycle modelling and stress-testing methods to support robust renovation strategies under future uncertainties,

Qualifications

• MSc in Building Technology, Civil Engineering, Architectural Engineering, or related field.
• Background in building performance simulation, lifecycle assessment, or climate resilience.
• Desirable knowledge of uncertainty quantification and risk modelling.
• Experience with programming and data analysis (e.g., Python).
• Fluent English with strong written and oral communication.

Responsibilities

• Develop probabilistic life-cycle modelling methods for renovation decision-making.
• Integrate climate risks, indoor comfort, sustainability goals and uncertainties.
• Collaborate with multiple research groups within TU Delft.
• Contribute to an explainable decision-support framework for stakeholders.

Skills

MSc in Building Tech

Python

English proficiency

Interdisciplinary research

Independent work

Education

MSc in Building Technology / Civil / Architectural Eng

Tools

Python

Job description

Delft University of Technology is inviting applications for a PhD position on Uncertainty-based Life-Cycle Modelling for Building Renovation Decision-Making. The project is part of Horizon Europe CLIMABUILD and spans 48 months, with collaboration across research groups in the Faculty of Architecture and the Built Environment.

The successful candidate will develop probabilistic life-cycle modelling and stress-testing methods to support robust renovation strategies under future uncertainties,