Sort by
Refine Your Search
-
Listed
-
Employer
- Umeå University
- Chalmers University of Technology
- KTH Royal Institute of Technology
- SciLifeLab
- Mälardalen University
- University of Lund
- Lunds universitet
- Umeå universitet stipendiemodul
- Blekinge Institute of Technology
- Karlstad University
- Karlstads universitet
- Karolinska Institutet (KI)
- Linköping University
- Swedish University of Agricultural Sciences
- The Swedish University of Agricultural Sciences
- Uppsala University
- Uppsala universitet
- 7 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
-
Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
-
and unsupervised pattern recognition problems as well as decision making tasks. Your duties will include reviewing relevant literature, designing and simulating neural network models, analyzing and
-
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
-
model developments, preparing and analysing observational datasets, designing experimental protocols, carrying out the related simulations and analysis, and leading writing of peer-reviewed papers based
-
superconducting-qubit readout systems, and demonstrating high-fidelity measurements using it as the only cryogenic amplifier. The work will involve device design and simulation, nanofabrication, cryogenic microwave
-
information for at least two academic references, and • a copy of the doctoral degree certificate and any other certificates or documentation the applicant wishes to be considered.
-
of the applicant must be specified) Copy of PhD degree or information on when it is expected to be completed Copy of doctoral thesis and other relevant publications An account of language skills Two references
-
dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
-
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