PhD Position - Artificial Intelligence for Reuse in the Built Environment

Take on the challenge of AI-driven design innovation by developing advanced computational methods to integrate reclaimed structural components into sustainable building solutions.

Job description

The waste from building demolition presents a significant societal, economic and environmental challenge. Part of this waste consists of fully functional structural components, which can be repurposed for new construction after minimal adaptation. Reintroducing these components into new buildings not only reduces waste but also lowers the CO2 emissions associated with producing new ones. Which strategies are most effective for designing with reclaimed structural components? How can computational methods assist designers in developing more sustainable design solutions?

Current research leverages optimisation methods to allocate reclaimed components within a predefined design. This top-down approach allows only certain parts of the design to be replaced; if no suitable components are available, the designer must rely on newly produced ones.

In contrast, this research explores a bottom-up approach in which design solutions are generated directly and exclusively from the available components. This approach presents unique computational challenges, such as managing the variability in component characteristics and stock composition, efficiently searching the design space, and simultaneously meeting several design requirements. To address these challenges, the research aims to develop innovative computational methods based on Artificial Intelligence (AI). Specifically, it will explore how AI, and particularly reinforcement learning, can support the design of structures that embed diverse reclaimed components while adapting to varying stock compositions.

The research will focus on investigating and developing computational methods for sustainable design. Research methods include analising precedents, developing models, conducting case studies, and performing validations. A key aspect of the research involves training AI models to address increasingly complex tasks, such as designing 2D trusses and 3D spatial frames that integrate single or multiple reclaimed component typologies. As part of this process, you will create environments for training the AI models, investigate strategies for representing reclaimed components based on their geometric and mechanical properties, and define metrics to evaluate the structural performance and environmental impact of AI-generated designs. You will later deploy the AI model as a design tool (e.g. as a CAD plugin), and prototype interfaces that enable designers to define design requirements, interact with the model, and compare different AI-generated design propositions within existing digital environments.

You will work in the Digital Technologies section of the Architectural Engineering and Technology department. This section focuses on the development and application of computational methods, tools, and techniques to sense, understand, model, design, and fabricate the built environment. Its approach integrates computational design and geospatial data, with key research areas including AI-supported and performance-based design, human-computer co-creation, design space exploration for decision-making, and human-robot interaction for creative robotics.

As a PhD researcher at TU Delft, you will be supported by leading experts in computational design and AI for structural design, who will guide the conceptualisation and development of the computational methods. You will also receive advice from specialists in engineering and construction technology to address the mechanical properties and integration of reclaimed structural components. Additionally, you will have access to high-performance computing infrastructures for AI training and structural performance simulations.

Job requirements

Required:

• You hold a MSc degree in Architecture, Civil Engineering, Computer Science or a related discipline.
• You are skilled in computational design methods and approaches, including AI for performance-based design.
• You have programming and software development experience.
• You are interested in design circularity and sustainable building practices.
• You have excellent academic writing skills.
• You have a good command of the English language in speaking, reading, and writing.

Preferred:

• You have experience applying reinforcement learning methods to building engineering problems.
• You have a proven track record of scientific publications on developing and applying various computational methods that address design problems.

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Architecture and the Built Environment

The Faculty of Architecture and the Built Environment has a leading role in education and research worldwide. The driving force behind the faculty’s success is its robust research profile combined with the energy and creativity of its student body and academic community. It is buzzing with energy from early in the morning until late at night, with four thousand people studying, working, designing, conducting research and acquiring and disseminating knowledge. Our faculty has a strong focus on 'design-oriented research’, which has given it a top position in world rankings.

Staff and students are working to improve the built environment with the help of a broad set of disciplines, including architectural design, urban planning, building technology, social sciences, process management, and geo-information science. The faculty works closely with other faculties, universities, private parties, and the public sector, and has an extensive network in the Netherlands as well as internationally.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2872 per month in the first year to € 3670 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.