Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Swedish University of Agricultural Sciences
- Umeå University
- Chalmers University of Technology
- Sveriges lantbruksuniversitet
- Linköping University
- Lulea University of Technology
- Lunds universitet
- Nature Careers
- Stockholms universitet
- University of Lund
- Uppsala universitet
- Jönköping University
- Umeå universitet
- University of Gothenburg
- Chalmers University of Techonology
- Fureho AB
- KTH Royal Institute of Technology
- Linnaeus University
- Luleå University of Technology
- Mid Sweden University
- Mälardalen University
- SciLifeLab
- 12 more »
- « less
-
Field
-
research in the areas of condensed matter physics, nanotechnology, photonics, and theoretical and computational physics. We announce a PhD position for a project focusing on simulation and artificial
-
research in the areas of condensed matter physics, nanotechnology, photonics, and theoretical and computational physics. We announce a PhD position for a project focusing on simulation and artificial
-
invites applications for a highly motivated PhD student to join our pioneering research in quantum simulations with trapped Rydberg ions. The aim of the PhD project is to develop a quantum simulator for
-
invites applications for a highly motivated PhD student to join our pioneering research in quantum simulations with trapped Rydberg ions. The aim of the PhD project is to develop a 2D ion trap experiment
-
Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
-
/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
-
, ABB robots with interchangeable grippers, distributed control systems, and testbeds for smart grids and real-time simulations. The project offers the opportunity to contribute to state-of-the-art
-
material layers that can be optimized to specific battery chemistries and flow phenomena from the microscale up. The developed technologies will be validated in half-cells and full working batteries
-
affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
-
. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material