March 2023: Fully Funded Postdoc Position starting September 2024
We have a fully funded Postdoc position in the field of Machine Learning for the Built Environment available, starting September 2024. The successful candidate will lead one of the lab’s current ML research projects. We offer full, competitive funding for 1 or 2 years, depending on the research project. More information
Febuary 2024: New paper: Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification
New paper by Austern, Bloch and Abulafya. The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information using Graph Neural Networks can improve classification accuracy. Full paper
August 2023: Fully Funded Postdoc Position starting March 2024
The candidate will head the lab’s research efforts on Applying Graph Neural Networks on BIM data, which is a part of the new Neuro-Symbolic AI in AEC research arc. In this research arc we join forces with researchers from the Stuttgart ICD as TU delft to envision and develop the next generation of BIM software. Our research will explore the use of Graph based Machine Learning models on Knowledge Graph representations derived from BIM files. We will use these models as the basis for a BIM autocomplete mechanism, which will allow BIM software to actively participate in the design process by making suggestions and filling in design details. More information
July 2023: Automation in Interior Space Planning - new paper in Buildings
Presenting a interior design tagging tool, a novel dataset and a comparison between Conditional Generative Adversarial Network Models (CGANs) as models for generating furniture layouts. Authors: Hanan Tanasra, Tamar Rott Shaham, Tomer Michaeli, Guy Austern and Shany Barath ,Full paper here: https://www.mdpi.com/2075-5309/13/7/1793
April 2023: Recycled Wood and Plastic Materilas - new research grant
Come join our growing research team! We have a fully funded Postdoc position researching Architectural Applications for Machine Learning available, starting October 2023. The successful candidate will head one of the labs’ current ML projects, on the urban, building or fabrication scale. Details
As part of the research project Developing Construction Products and Processes from Recycled Waste Materials, the CD_ML lab and the DD lab at the Faculty of Architecture and Town Planning at Technion are recruiting several Doctoral / Master’s fully funded researcher positions. The project will harness the environmental potential of recycled wood/polymer materials (RWPM) by developing building products and fabrication processes using additive manufacturing and pressurized casting.
The project is funded by the Israel Innovation Authority. A generous monthly stipend will be paid to the researchers according to their qualifications and experience.
Application is open until 10th of February 2023, for more details see: 2023-RWPM
December 2022: ML in the AEC industry - new research grant
The CD_ML is starting work an a research grant aimed at investigating the application of Machine Learning in the context of the AEC Industry. Funded by the Ministry of Construction and Housing of Israel - משרד הבינוי והשיכון. Co-researchers Yasha (Jacob) Grobman Tanya Bloch and myself will be investigating the needs of the Israeli construction industry and the adoption of ML models in the contech ecosystem. Among the products of the research will be an initial dataset of Israeli buildings. Stay tuned!
August 2022: Real Time Fabrication Analysis - new paper in Architectural Science Review
Proud to present the most recent paper of the fabrication rationalization research arc:
Austern, Guy, Isaac Guedi Capeluto, and Yasha Jacob Grobman. “Real-Time Fabrication Analysis: A Method for Evaluating Fabrication Constraints of Complex Concrete Shapes.” Architectural Science Review (August 6, 2022).
link to paper