Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Lunds universitet
- Linköping University
- Umeå University
- Uppsala universitet
- SciLifeLab
- Umeå universitet
- Linköpings universitet
- KTH
- Lulea University of Technology
- University of Borås
- Chalmers tekniska högskola
- Jönköping University
- Linkopings universitet
- Luleå University of Technology
- Nature Careers
- Stockholms universitet
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- Umeå universitet stipendiemodul
- University of Lund
- Örebro University
- 13 more »
- « less
-
Field
-
to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
-
multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
-
numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
-
, or has experience with optimization algorithms and with improving the efficiency of computational methods. The workplace Linköping University is one of the leading AI institutions in Sweden. We have strong
-
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
-
support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
-
into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive
-
imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
-
algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we are looking for The following requirements are mandatory: A doctoral
-
of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with