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School of Industrial Engineering and Management

Our core knowledge areas include industrial design and innovation, product and production development, materials development, energy technology, learning in engineering sciences as well as industrial economics, organisation and management. Our efforts are directed towards a green and sustainable society for the future.

Departments 

Campus relocation

KTH has decided to relocate the educational programmes and teaching staff at the Södertälje campus to the KTH campus and KTH Flemingsberg. The relocation will be gradual and will begin in the spring semester of 2025 for parts of the operations. From 2027, KTH will conduct its remaining activities and collaboration with strategic partners such as Scania and Astra-Zeneca in new forms.

More about the campus relocation for students

More about the campus relocation for employees

Latest news

Scania truck on city street
Photo: Scania

The HITS project shows great potential for sustainable urban transport

Cities across Europe aim to reduce street traffic for a better environment and lower climate impact. A research study initiated by Scania within the HITS project, shows that urban transport can be tra...

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Participants 'program' their own apartments during the workshop.

How can we make shared housing happen?

This was the theme for a policy workshop to identify hinders and create solutions to support building shared housing in Sweden.

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Björn Palm at COP13
Björn Palm is a senior professor of Energy Engineering who has been researching mainly heat pumps and cooling systems since the 1990s.

KTH helps maintain Montreal Protocol

Björn Palm, Professor at KTH and pioneer in refrigerants, you presented at COP13/MOP36 a few days ago. What did you say?

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Calendar

Publications

[1]
Z. Zhou et al., "Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[2]
M. Sun et al., "Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[3]
Y. Qin et al., "A tool wear monitoring method based on data-driven and physical output," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
Full list in the KTH publications portal