Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Chalmers University of Technology
- Karolinska Institutet (KI)
- Lunds universitet
- KTH Royal Institute of Technology
- Uppsala universitet
- Linköping University
- Nature Careers
- SciLifeLab
- Umeå universitet stipendiemodul
- Lulea University of Technology
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- Umeå University
- Umeå universitet
- Blekinge Institute of Technology
- Luleå University of Technology
- Örebro University
- Högskolan Väst
- IFM, Linköping University
- IFM/Linköping University
- Linnaeus University
- Linneuniversitetet
- Luleå university of technology
- Mälardalen University
- SLU
- Stockholms universitet
- University of Lund
- 17 more »
- « less
-
Field
-
management. Data from case studies (inspections, monitoring, and experimental tests) are used for model updating, calibration of safety formats, and prediction of future performance and remaining service life
-
to enable collaborative work between the two strategic research areas eSSENCE and NanoLund (https://www.essenceofescience.se/ and https://www.nano.lu.se/ ), which means that the postdoc will benefit from
-
Systems and Control division focusing on data-driven control methodologies. About the research project Model-based control is arguably the prime framework to perform certifiably-safe regulation of dynamical
-
advances focused microwave heating as a safe, quantifiable, and effective cancer treatment modality. Using a numerical model of our hyperthermia system, we are the first group worldwide to demonstrate
-
focused on modeling and simulations of turbulent mixed-phase clouds. This interdisciplinary project encourages collaboration with experts in atmospheric physics. The successful candidate will contribute
-
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
-
postdoc to join our team at the Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, and contribute to research on stochastic and statistical models for large-scale shape
-
transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence
-
Join us to pioneer next-generation generative models that accelerate molecular dynamics. We seek a postdoctoral researcher to develop AI surrogates for molecular dynamics (MD), slashing
-
understanding of slag modification routes and their implications for material performance. The research combines thermodynamic modelling, laboratory-scale experiments, and advanced slag characterization