Are you eager to work on scalable building energy modelling to support sustainable urban districts? Join us in developing data-driven typologies and simulation methods that help shape the energy-positive cities of the future.
InformationWe are seeking a motivated PhD researcher to contribute to the development of scalable and efficient methods for building energy modelling to support
Positive Energy Districts (PEDs).
In this position, you will develop a
systematic, data-driven approach to identify and model representative building typologies based on public datasets such as BAG, 3DBAG, CBS, satellite imagery, and OpenStreetMap. Your work will focus on enabling the efficient generation of building energy models (BES) at the neighborhood scale, using clustering and sensitivity analysis to reduce model complexity without compromising accuracy. You will also contribute to the development of automated workflows for model creation.
This PhD is part of the
EmPowerED research project, which brings together researchers, municipalities, grid operators, housing associations, and citizens to support the design of
sustainable, affordable, and widely supported local energy systems. EmPowerED aims to accelerate the energy transition by placing citizens at the heart of PED development through new socio-technical models and tools. You will collaborate closely with a second EmPowerED PhD, who focuses on aligning model complexity with the specific needs of use cases defined by project stakeholders.
You will be embedded in the
Building Performance research group and the
Information Systems in the Built Environment research group, working in a collaborative and interdisciplinary environment. Your contributions will support municipalities and practitioners in making informed decisions about building performance, energy use, and renovation planning.
We welcome applicants from diverse backgrounds who are enthusiastic about working at the interface of
building engineering, data analysis, and building energy modelling.