Sort by
Refine Your Search
-
Employer
-
Field
-
Join us at the National Centre of Excellence CHARMEC for an exciting and excellent PostDoc journey to investigate hybrid asset management on railways! About us Your workplace will be at the Division
-
of the electrolyte solvents. The postdoc is expected to perform high-quality research as part of a team, with the aim of disseminating resulting works through high impact peer-reviewed journals, and at national and
-
. About the research project Wind-powered propulsion is a key route to reducing emissions from shipping, but the interaction between the flow around a wind propulsion system and the structural response
-
resource-efficient and sustainable? We are looking for a postdoc at the competence centre Svenskt Krut at KTH, where academia, government agencies and industry jointly strengthen Sweden's capability in
-
working conditions? We welcome you to apply for a postdoc position at Uppsala University. Uppsala University is a comprehensive research-intensive university with a strong international standing. Our
-
imaging applications. As a PostDoc you will contribute to the evaluation, development, validation and deployment of advanced data reduction and compression methodologies for large-scale imaging datasets
-
GPU infrastructure. This Postdoc position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational
-
operated by the Marine Infrastructure at the University of Gothenburg. The current employment is based at Natrium, with the possibility to conduct research projects at the marine station. We offer
-
years Period of employment/Start date: As agreed Scope: Full time Number of positions: 1 Closing date for application: 26 october 2026 Campus location: Eskilstuna School: Teknikvetenskap Position
-
candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification