Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- SciLifeLab
- KTH Royal Institute of Technology
- Blekinge Institute of Technology
- Umeå University
- Linköping University
- The Faculty of Technology and Society
- Uppsala universitet
- Lunds universitet
- University of Lund
- Luleå University of Technology
- Swedish University of Agricultural Sciences
- Lulea University of Technology
- Umeå universitet stipendiemodul
- University of Skövde
- Uppsala University
- Örebro University
- Göteborgs universitet
- Helmholtz-Zentrum München
- Karolinska Institutet, doctoral positions
- Linnaeus University
- The Swedish University of Agricultural Sciences
- The University of Skövde
- Umeå universitet
- University of Borås
- 15 more »
- « less
-
Field
-
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
-
is BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and
-
architecture, parallel systems, programming language theory, embedded systems or software engineering. Documented ability to teach operating systems and compiler construction. Practical experience of systems
-
of the central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding. The goal is to develop new
-
, reliability, model-based AI, machine learning, and semantic or task-driven methods, within the group’s established research agenda. About the division and department At the Department of Electrical Engineering
-
of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
-
sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and master’s levels, as well as the programmes in
-
and machine learning, digital health, and advanced signal processing, with applications in healthcare, autonomous systems, industry, and energy. Through interdisciplinary research, we contribute
-
of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
-
Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our growing research