Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Umeå University
- SciLifeLab
- University of Lund
- Lunds universitet
- Mälardalen University
- Swedish University of Agricultural Sciences
- Jönköping University
- Uppsala universitet
- Karlstad University
- Örebro University
- Blekinge Institute of Technology
- Karolinska Institutet, doctoral positions
- Linköping University
- Linnaeus University
- Luleå University of Technology
- The Swedish University of Agricultural Sciences
- Umeå universitet stipendiemodul
- University of Skövde
- European Magnetism Association EMA
- Göteborgs universitet
- Institutionen för Biologi och miljövetenskap
- Karlstads universitet
- Karolinska Institutet (KI)
- Linköping University (LiU)
- Lulea University of Technology
- Luleå tekniska universitet
- Stockholm University
- Sveriges Lantbruksuniversitet
- The Swedish University of Agricultural Sciences (SLU)
- Umeå universitet
- University of Gothenburg/Department of Biological and Environmental Science
- 23 more »
- « less
-
Field
-
learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
-
and application of methods for simulating magnetism, particularly atomistic and multiscale simulations. The successful candidate will have the opportunity to collaborate with leading experimental and
-
development, assembly, verification, and validation through simulation and experimental testing. The research engineer will also participate in the planning and execution of experiments, analysis of results
-
installed at GANIL. You will contribute to the simulation, preparation, optimisation, and further development of the experimental setup and its associated systems. You will also take an active role in
-
of the system is still insufficiently understood. This project investigates the underlying fluid-structure interaction mechanisms and develops advanced numerical methods for high-fidelity simulation
-
simulation and AI-supported data analysis are central tools. The work is carried out at the Department of Fibre and Polymer Technology and in collaboration with FOI and industrial partners. Qualifications
-
focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs, and complete battery systems. Thermal runaway is a chain
-
copy of your Master’s degree certificate and course transcripts, as well as a copy of your Master’s thesis, 5) any other documents you wish to refer to, 6) the names and contact details of referees
-
. The application must also include a CV, a copy of your doctoral degree certificate, and a list of publications.
-
through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and