Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Jönköping University
- Karlstad University
- Linköping University
- Umeå University
- Umeå universitet stipendiemodul
- IFM/Linköping University
- Linköpings universitet
- Lulea University of Technology
- Luleå University of Technology
- Mälardalen University
- Nature Careers
- SciLifeLab
- Sveriges Lantbruksuniversitet
- Umeå universitet
- University of Lund
- 6 more »
- « less
-
Field
-
Computer Vision algorithms. Experience using urban building stock modelling and urban digital twins What you will do: Design & Develop: Create data structures for detailed, spatialised construction component
-
application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
-
Are you excited about pioneering experimental quantum computing? Do you want to be part of a world-class research environment developing the next generation of superconducting quantum processors? We
-
, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
-
Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
-
, algorithms, data or automation affect the public sector, preferably from a critical perspective. Assessment criteria This is a career development position primarily focused on research. The position is
-
this project, we will develop new algorithms and computational schemes as well as further develop existing computational frameworks in the team. We will focus on two related frameworks in the project
-
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
-
they can contain traces 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
-
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