WASP – Wallenberg AI, Autonomous Systems and Software Program

Robotics and AI and machine learning are the two research areas at Luleå University of Technology that are linked to WASP.

The research group in robotics and AI wants to bring robotics into people's everyday lives and create a continuous impact on human well-being. They want to close the gap between advanced theory and real-world applications. Examples of applications are found, for example, in the mining industry, in the inspection of aging infrastructure, aircraft and wind turbines, in autonomous aerial and ground vehicles, robots for security and surveillance, and in space robotics.

The machine learning research group focuses on three major sustainability goals: sustainable industrial innovation and infrastructure, good health and well-being, and education and infrastructure. They contribute to the goals through fundamental and application-oriented research in machine learning and artificial intelligence, deep learning, pattern recognition and human computer interaction. But also with applications in digital humanities, education, document analysis and Industry 4.0.

ABOUT WASP

Wallenberg AI, Autonomous Systems and Software Program, is a national initiative for strategically motivated basic research, education and faculty recruitment.

The research in WASP can be illustrated as a matrix with two dimensions, a strategic dimension and a thematic dimension. The strategic dimension emphasizes areas of impact on individuals, society and industry, while the thematic areas represent the underlying scientific and technological challenges common to all types of autonomous systems.

The research is conducted at eight Swedish universities: Chalmers University of Technology, KTH, Linköping University, Lund University, Umeå University, Örebro University, Uppsala University and Luleå University of Technology.

Projects

• Generative AI-Driven Framework for Optimal Structure Selection and Generation in Material Science
• Machine Learning assisted engineering of artificial microbial consortia for the delivery of bio-plastic materials from renewable waste
• Fast, Robust, Efficient NanoPhotonic Neuromorphic Computing
• Modeling REE interactions by Explainable Ais

Research

Reading between ancient lines with machine learning