Sort by
Refine Your Search
-
Employer
- Chalmers University of Technology
- Linköping University
- Umeå University
- IFM/Linköping University
- Jönköping University
- Karlstad University
- Linköpings universitet
- Lulea University of Technology
- Luleå University of Technology
- Mälardalen University
- Nature Careers
- SciLifeLab
- Umeå universitet
- Umeå universitet stipendiemodul
- Uppsala universitet
- 5 more »
- « less
-
Field
-
of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental studies
-
objective of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental
-
-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
-
, modulation classification, sensing, and adaptive spectrum optimization in diverse operational environments. Your work will focus on modeling and algorithmic aspects related to the development of highly
-
plan (e.g., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and
-
., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and modeling efforts
-
each year. You can find more information about us on the Department of Information Technology website. At the Division of Systems and Control in the Department of Information Technology, we develop both
-
will be as a researcher in a two-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault
-
-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault diagnosis of gas turbines
-
algorithmic aspects related to the development of highly accurate, efficient, and robust AI models capable of operating effectively within complex and dynamic radiofrequency spectral landscapes, accounting