RT @NSMDresearch: Two days of training on network analyses at Hotel Arena in Amsterdam just started. With @gerasimoss, @EikoFried, @jonasha…
Founded in 1992, DKE is a fast-growing department undertaking research to advance the fields of artificial intelligence, data science, computer science, applied mathematics and robotics. Furthermore, DKE maintains a large network of industry partners through the award-winning KE@Work programme and through our research collaborations.
The department provides education through one bachelor’s programme and two master’s programmes.
Research at the Department of Data Science and Knowledge Engineering spans the disciplines and interfaces of artificial intelligence, data science, computer science and applied mathematics.
We develop new tools and methodologies to advance these fields. At the same time, we collaborate with a wide range of institutes both within and outside of Maastricht University and work on diverse applications, including in the fields of health and medicine, logistics, biology, art, physics, cybersecurity, neuroscience and education.
Our 3-year bachelor's programme offers a combination of artificial intelligence, computer science and mathematics.
Our 2-year master's programme in Data Science for Decision Making teaches students to extract valuable information from large datasets for widespread applications.
Our 2-year master's programme in Artificial Intelligence focuses on simulating human intelligence for a wide variety of applications: from game design to patient diagnosis.
We also offer an exchange programme for students looking to study abroad.
Congratulations to Anna-Lena Krause, Krzysztof Cybulski and Frederik Calsius!
As of 1 April 2022, David Groep holds an endowed chair at the Department of Data Science and Knowledge Engineering. His work revolves around the complex, large-scale ICT infrastructures that provide a foundation for similarly cutting-edge research.
Maastricht University, Nikhef, CERN and SURF will address some specific challenges for the LHCb experiment in order to determine which elements of the data analysis chains are best suited for a quantum computing approach.