AI for Biosciences Lab (AI4B.io Lab) is a collaboration between Delft University of Technology and DSM. The lab focuses on improving production technologies and developing bio-based products using AI. AI4B.io Lab is the first of its kind in Europe to apply artificial intelligence to full-scale biomanufacturing, from microbial strain development to process optimization and scheduling. AI4B.io Lab is aimed at long-term innovation in the domain of AI for developing biobased products and optimizing biobased production technologies. It targets to develop a deep understanding of how novel AI technology (methods, techniques, theories, and algorithms) can strengthen the effectiveness and efficiency of relevant research and/or business processes in the biotech industry.
AI for Fintech Research (AFR) is a collaboration between ING and Delft University of Technology. The mission of AFR is to perform world-class research at the intersection of Artificial Intelligence, Data Analytics, and Software Analytics in the context of FinTech. Over the next five years, ten PhD researchers will work in the lab on projects that will focus, among other things, on autonomous software engineering, data integration, analytics delivery, and continuous experimentation.
The AI for Retail (AIR) Lab Delft is a joint TUDelft-Ahold Delhaize industry lab consisting of a robotics research program and test site focused on developing state-of-the-art innovations in the retail industry. By expanding its focus to robotics, AIRLab Delft will further drive innovations for daily business while building more knowledge of the intersection between retail, AI and robotics. The expansion comprises a robotics research program and test site for developing state-of-the-art innovations in the retail industry.
The Mercury Machine Learning Lab is a collaboration between University of Amsterdam, Delft University of Technology and Booking.com. The lab focuses on the development and applications of artificial intelligence to the specific domain of online travel booking and recommendation service systems. The research projects cover fundamental research topics, ranging from model-based exploration, parallel model-based reinforcement learning, methods for combined online and offline evaluation, prediction methods that correct for undesired feedback loops and selection bias, domain generalization and domain adaptation, and novel language processing models for better generalization. These topics are both of fundamental scientific importance, as well as of immediate practical relevance for modern online businesses like Booking that aim to maximize customer satisfaction in quickly changing markets with the help of sophisticated data analytics.