Sort by
Refine Your Search
-
Listed
-
Employer
- SciLifeLab
- Karlstad University
- Umeå University
- Blekinge Institute of Technology
- Linköping University
- Chalmers University of Technology
- University of Lund
- Karlstads universitet
- Linnaeus University
- Mälardalen University
- Nature Careers
- Karolinska Institutet (KI)
- Lunds universitet
- Swedish University of Agricultural Sciences
- University of Gothenburg
- Örebro University
- 6 more »
- « less
-
Field
-
of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
-
master's level courses in machine learning and R programming during the autumn semester of 2025 (with a possibility of extension). The main tasks involve assisting students during lab sessions and possibly
-
education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university as a workplace Umeå Institute of Design is
-
through the application of both well-established statistical modelling and newer machine learning methods. The research specialist will be integrated in the computational team led by John Wallert
-
intelligence, robotics, machine learning, and human-robot interaction. Project Description The focus of the project is artificial intelligence (AI) and its relation to robotics and embodiment. Embodiment plays a
-
spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
-
interdisciplinary approach encourages contributions to related projects, including applications of machine learning to autoimmune disease and non-invasive diagnostics using cell-free nucleic acids. Duties Develop and
-
). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
-
advising on methods and systems, assessing quality and properties of data, assisting with resource allocation proposals, machine learning workflows, dataset curation, organization, and sharing, data
-
of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction