Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Karolinska Institutet (KI)
- Lulea University of Technology
- Uppsala universitet
- Blekinge Institute of Technology
- Linköping University
- Luleå University of Technology
- Lunds universitet
- Umeå universitet
- University of Lund
- 1 more »
- « less
-
Field
-
18 Feb 2026 Job Information Organisation/Company Luleå University of Technology Research Field Engineering Researcher Profile Recognised Researcher (R2) Application Deadline 17 Mar 2026 - 12:00 (UTC
-
areas is considered a strong merit: Aircraft design (concpetual design and performance evaluation) Propulsion systems (performance, and integration) Multidisciplinary modelling (combining engine
-
post-doc position for you! About us The Department of Space, Earth and Environment brings together expertise in space, geoscience, energy and sustainability. Through curiosity-driven research, education
-
to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering ! About us Our
-
Luleå University of Technology experiences rapid growth with world-leading expertise within several research areas. Our scientific and artistic research and education are conducted in close
-
at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
-
dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A doctoral
-
on the development and application of advanced modelling and machine learning methods, and may involve the following areas: Dimensionality reduction. Data-driven methods for estimating dynamical models Data-driven
-
learning, multivariate modeling, or data-driven approaches, as well as interest or experience in the integration of neuroimaging with genetic or transcriptomic data, is considered a strong merit. Prior
-
-induced variability, residual stresses, surface integrity, inspection and qualification requirements, and sustainability. Capturing these effects in whole-engine, system-level models remains an open