Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- DTU Electro
- AALTO UNIVERSITY
- ; Swansea University
- AMOLF
- Aalborg University
- Curtin University
- DAAD
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- Leibniz
- NTNU Norwegian University of Science and Technology
- Swansea University;
- The University of Manchester;
- University of Groningen
- University of Newcastle
- Vrije Universiteit Brussel
- Wetsus - European centre of excellence for sustainable water technology
- 8 more »
- « less
-
Field
-
expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently
-
University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
-
on performance, safety, and robustness of robotic and learning-enabled systems. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear
-
the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Work Activities We
-
Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 1 month ago
Research Infrastructure? No Offer Description Topic background A proper functioning drinking water distribution network is of major importance for society and keeping this network optimally operational is
-
control systems. It will address practical limitations that prevent reaching theoretical performance, with particular emphasis on optimal feedback design, actuator optimization, and novel control strategies
-
University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
-
, these models often use simplified, linearized assumptions, limiting their capacity to capture the nonlinear complexities inherent in real-world hydrological processes. Recently, there has also been the branch
-
Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics
-
scales and different phases which leads to nonlinear time and history dependent material behavior. Additionally, innovative changes are happening in the steel production process, especially in the drive