Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Umeå University
- Linköping University
- SciLifeLab
- Swedish University of Agricultural Sciences
- Uppsala universitet
- Lulea University of Technology
- Lunds universitet
- Luleå University of Technology
- Luleå tekniska universitet
- Blekinge Institute of Technology
- Karolinska Institutet, doctoral positions
- Mälardalen University
- Stockholms universitet
- The Swedish University of Agricultural Sciences
- Umeå universitet
- University of Lund
- Chalmers University of Technology
- Göteborgs universitet
- Institute of Neuroscience and physiology, Sahlgrenska Academy, university of Gothenburg
- Institutionen för Biologi och miljövetenskap
- Institutionen för mark och miljö
- Jönköping University
- KTH Royal Institute of Technology
- Linnaeus University
- Sveriges Lantbruksuniversitet
- The Swedish University of Agricultural Sciences (SLU)
- 16 more »
- « less
-
Field
-
Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
-
application! We are now looking for 1–2 PhD students for the Division of Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Within the research unit
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with nuclear fuel modelling, machine learning
-
. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
-
computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP
-
perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
-
software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
-
, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
-
. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
-
Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work