Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Umeå University
- SciLifeLab
- The Faculty of Technology and Society
- Lunds universitet
- Umeå universitet stipendiemodul
- Blekinge Institute of Technology
- Linköping University
- Lulea University of Technology
- Luleå University of Technology
- Umeå universitet
- University of Borås
- University of Lund
- University of Skövde
- Uppsala universitet
- universitypositions
- Örebro University
- 8 more »
- « less
-
Field
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
-
proven experience, an area that has been strengthened by the national initiative ULF (Development, Learning, Research). Learn more here: https://www.umu.se/en/department-of-creative-studies/research
-
. The following experience will strengthen your application: industrial product development or manufacturing research modelling and simulation, digital twins or digital threads AI, machine learning
-
energy-efficient and sustainable transport systems through world-class research in tribology and machine elements. Friction losses in vehicle systems still account for a significant portion of global
-
domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
-
, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and
-
description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
-
at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
-
aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal/background discrimination. This will be a focus in the project. One exciting