Sort by
Refine Your Search
-
Employer
- KTH Royal Institute of Technology
- Chalmers University of Technology
- SciLifeLab
- Umeå University
- University of Lund
- Lunds universitet
- Karolinska Institutet (KI)
- Lulea University of Technology
- Umeå universitet stipendiemodul
- Blekinge Institute of Technology
- Institutionen för biologi och miljövetenskap
- Jönköping University
- Linköping University (LiU)
- Luleå University of Technology
- Luleå tekniska universitet
- The Swedish University of Agricultural Sciences
- University of Skövde
- Uppsala University
- Uppsala universitet
- universitypositions
- 10 more »
- « less
-
Field
-
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
-
infrastructure, mobility demand, and power grid operations. On top of this environment, a deep-learning-based learning will be developed to enable decentralized and coordinated decisions on EV user charging
-
constellation of SciLifeLab researchers and infrastructure units. This position is embedded in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab. Our lab develops deep learning models
-
the opportunity for three weeks of training in higher education teaching and learning. The job also includes opportunities for teaching on basic and advanced level. participation in supervision of master and PhD
-
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
-
will also have good opportunities to learn from and socialize with other postdoctoral fellows at the department. For more information about the department or division visit: https://www.slu.se/om-slu
-
, multi-omics analyses and systematic bioinformatics techniques is a strong advantage. Excellent programming skills (Python/R) and a solid training in AI or machine learning are highly preferred. A strong
-
levels of curiosity, independence, and excellent organisational skills High levels of critical thinking, problem solving, and a general drive to learn new concepts and methods Advanced skills in
-
the division of Engineering Logistics, Department of Industrial and Mechanical Sciences. We have approximately 15 employees. Here we teach and conduct research in the field of logistics and supply chain
-
agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues