Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Lunds universitet
- University of Lund
- KTH Royal Institute of Technology
- Karolinska Institutet (KI)
- Umeå universitet stipendiemodul
- Linköping University
- SciLifeLab
- Umeå University
- Nature Careers
- Uppsala universitet
- KTH
- Jönköping University
- Lulea University of Technology
- Sveriges Lantbruksuniversitet
- Umeå universitet
- Blekinge Institute of Technology
- European Magnetism Association EMA
- Göteborgs universitet, Department of Marine Sciences
- Högskolan Väst
- IFM, Linköping University
- IFM/Linköping University
- Mälardalen University
- SLU
- Stockholms universitet
- Örebro University
- 16 more »
- « less
-
Field
-
-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
-
Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
-
disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
-
for a postdoc to join our team at the Division of Systems and Control, Department of Electrical Engineering. Become part of our innovative group and contribute to exciting research in learning-based
-
learning-based control within a collaborative and dynamic environment. About us At the department of Electrical Engineering research and education are performed in the areas of Communications, Antennas and
-
). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
-
risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
-
materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
-
at: https://www.umu.se/en/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 models
-
-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data