TU Delft AI Labs

An important part of the TU Delft AI Initiative is the establishment of a total of 24 labs in which experts in the field of AI foundations work together with experts who work on societal and scientific challenges with the help of AI. The TU Delft AI labs are an embodiment of the bridges that are built between science in and with AI, Data and Digitalisation.

This approach gives a significant boost to education and increases the impact of Delft AI on science, technology and society. The first 16 labs have already been established, the next eight will follow this year.

The first sixteen out of twenty-four TU Delft AI-Labs

3D Urban Understanding. New methods to automatically recognise and model objects in the built environment in 3D.

AI for Design, Analysis and Optimisation in Architecture & the Built Enviroment. AI for a sustainable and resilient built environment.

AI for sustainable water management. Enhancing the adaptability and resilience of urban water systems.

AIFluids Lab
Artificial Intelligence in Fluid Mechanics. Using AI and fluid mechanics to build better planes and wind farms.

Transparent & Traceable AI in Human-AI Teamwork. Developing optimal and transparent decision making in human-AI teamwork.

A place where data, AI and behavioral theory come together. Translating data into insights into the structure of cities and its impact on human behaviour.

DAI energy Lab
AI for sustainable, reliable and effective energy systems. New AI-based methods that contribute to managing (dynamic) energy systems.

Design @ Scale Lab
Humans and AI tackle problems together. Integrating Participatory Design, Crowd Computing and AI to better address complex social problems.

DeTail Lab
Training & innovation in tensor-based AI methods for biomedical signals. Fundamental development of tensor computing and its applications.

Humans and AI designing the future in dialogue. Using AI in the development of new design methods.

Human-aware robust AI for automated driving. New AI methods to enable automated driving systems to interact with the humans around them.

Hippo DAI Lab
AI for fair, efficient and interpretable policy analysis. Optimisation methods for the fair and efficient design and analysis of public policies.

AI for quantitative bioimaging. AI-based technology that improves microscopy methods for biomedical use.

From data-driven to knowledge-driven AI. Strengthening data-driven AI by integrating fundamental knowledge from applied natural sciences.

Machine Intelligence Advances for Materials. Creating a new route for designing novel materials and AI algorithms.

AI for smart materials modelling. Developing Bayesian inference tools specifically for application

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