Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Technical University of Denmark
- DAAD
- Forschungszentrum Jülich
- Chalmers University of Technology
- Curtin University
- Ghent University
- La Trobe University
- Max Planck Institutes
- Mid Sweden University
- Nature Careers
- Technical University Of Denmark
- The University of Manchester;
- Umeå University
- University of Adelaide
- University of Groningen
- University of Nebraska–Lincoln
- Warsaw University of Technology
- 7 more »
- « less
-
Field
-
Is 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 Topic: CARE
-
two-dimensional materials: spectroscopic investigations of two-dimensional semiconductors, measurements and analysis using linear and non-linear microscopy. The scientific work further includes
-
materials: spectroscopic investigations of two-dimensional semiconductors, measurements and analysis using linear and non-linear microscopy. The scientific work further includes collaborations with national
-
, statistical physics, fluid mechanics, phase transitions, non-linear dynamics and chaos! Doctoral Insights Symposium Sign up here to learn more about us and other structured PhD Programs across Europe! Applied
-
memristive devices, in collaboration with PGI-14. The goal is to train a system that leverages the intrinsic non-linear dynamics of these devices to perform complex learning tasks with extreme energy
-
neuromorphic hardware, this project will push into next-generation analog circuits and memristive devices, in collaboration with PGI-14. The goal is to train a system that leverages the intrinsic non-linear
-
responsibilities and tasks include (but not limited to): Development of full- and reduced-order nonlinear finite element models of offshore structures with emphasis on damage modelling. Calculation of linear and
-
. While applicants are not expected to meet all criteria, those who demonstrate more of the following attributes will be highly regarded: Strong foundation in AI models: A deep understanding of contemporary
-
of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks
-
) the development of a holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3