Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- Eindhoven University of Technology (TU/e); Eindhoven
- AMOLF
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); yesterday published
- Erasmus University Rotterdam
- University of Groningen
- University of Twente (UT)
- University of Twente (UT); Enschede
- Wetsus - European centre of excellence for sustainable water technology
- 2 more »
- « less
-
Field
-
Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering Job description Nonlinear dynamics lies at the centre of many mechanical systems, from large-scale structures to nanoscale
-
Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Harnessing Nonlinear Dynamics: From Data-Driven
-
23 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Civil engineering Engineering » Mechanical engineering Researcher Profile
-
of the global supply chain. Most ways to model the stochasticity involve introducing nonlinear terms in the mathematical formulation. It is your goal to investigate how well machine learning methodology can be
-
, this challenge is considered for a particularly important class of systems, namely second-order structural dynamics systems with nonlinearities, often encountered in mechatronic and robotic applications. You will
-
This position is part of the NWO KIC Smart Materials project, Smart Materials for Information Processing, in collaboration with the NanoElectronics (NE) group at the University of Twente and the
-
in a 5th generation district heating network. The key weakness of most models currently available and in use is their oversimplified description of physical, dynamic and nonlinear behavior
-
remanufacturing. Help shape the future of sustainable, high-performance production. Information Additive manufacturing (AM) is transforming industrial production by enabling the creation of lightweight, customized
-
such as model-based optimal control and nonlinear reset control. The goal is to push beyond commercial standards, achieving unprecedented sensitivity by overcoming mechanical and interferometric noise
-
without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog