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Deep Learning for Architectural Design Exploration

   School of the Built Environment

  , Dr V Ojha  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

"Generative Adversarial Networks (GANs) are an emerging research area in deep learning that have demonstrated impressive abilities to synthesize designs, however their application in architecture has been limited, especially for 3D applications (Newton, 2019). This project will involve the application of supervised machine learning in architectural design, developing novel research in the field of computational architecture. The focus will be in the application of Convolutional Neural Networks (CNNs), including Generative Adversarial Networks (GANs), in 3d urban analysis and masterplan generation on real projects. Working within the Urban Living Research Group here at the School of Architecture, the project will investigate how GANs can assist humans (without replacing them) in the design of urban space with a focus on 3D applications beyond 2D networks on real project sites within the Reading/London area. Supervision will be cross-disciplinary between Architecture and Computer Science.

Duration of study: Full-time or Part-time.

Please contact Dr John Harding for further information.

Funding Notes

A Masters' degree in a relevant subject, or equivalent professional experience is desired. Some programming experience in relation to generative art/architecture preferable.


Newton, D. (2019) Generative Deep Learning in Architectural Design, Technology|Architecture + Design, 3:2, pp.176-189."

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