16 phd-in-architecture-interior-design-built-environment PhD positions at Lulea University of Technology
Sort by
Refine Your Search
-
Category
-
Field
-
using data-driven approaches. You will be based in the Entrepreneurship and Innovation group—one of Sweden’s leading research and teaching environments—where you will collaborate with experienced faculty
-
and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
-
and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
-
applications supporting AR/VR/XR in the context of climate change and sustainability. Duties As a PhD student you are expected to perform both experimental and theoretical work within your research studies as
-
applications supporting AR/VR/XR in the context of climate change and sustainability. Duties As a PhD student you are expected to perform both experimental and theoretical work within your research studies as
-
University of Technology, is now looking for a PhD student to contribute to our growing activities. The RAI team is conducting fundamental research in all the aspects of robotics with a specific focus on
-
, Electrical and Space Technology is seeking PhD candidates to contribute to our growing activities. The position is located at the Division of Space Technology in Kiruna. The division offers a number of Master
-
and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
-
University of Technology, is now looking for a PhD student to contribute to our growing activities. The RAI team is conducting fundamental research in all the aspects of robotics with a specific focus on
-
part of the MARTINA project and will explore the application of co-design optimized machine learning and neuromorphic solutions for applications that are challenging to address using conventional