Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Chalmers tekniska högskola
- Lunds universitet
- University of Lund
- Uppsala universitet
- SciLifeLab
- Linköping University
- Umeå University
- Nature Careers
- Karolinska Institutet (KI)
- Lulea University of Technology
- Luleå University of Technology
- Lund University
- Stockholms universitet
- University of Gothenburg
- Blekinge Institute of Technology
- Chalmers
- Chalmers Tekniska Högskola AB
- Chalmers University of Techonology
- Chalmers tekniska högskola AB
- Fureho AB
- KTH
- Karlstad University
- Malmö universitet
- Swedish University of Agricultural Sciences
- Umeå universitet
- Umeå universitet stipendiemodul
- 18 more »
- « less
-
Field
-
focuses on developing advanced optimization and control strategies (e.g., deep reinforcement learning) for large-scale sustainable grids, to enhance overall system stability, flexibility, and resilience
-
properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
-
apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
-
learning, optimization algorithms, and interoperability frameworks for optimal energy management across Europe. KTH leads technological landscape analysis, multi-energy investment planning tool development
-
sustainability but also pose serious challenges in ensuring their reliability and fairness. Addressing these societal-scale challenges demands for novel optimization and control methodologies that can meet their
-
/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
-
research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows, innovative technologies for biomass conversion, neural network systems, and artificial
-
the Department of Energy Sciences. At the division, we conduct research in various fields, including research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows
-
(LLM), and optimization algorithms. Collaborating with our team to transform research insights into practical, impactful solutions. Staying abreast of the latest advancements in ML, transformers, graph
-
collaborate on modelling and numerical optimization in robotics, electromobility, and autonomous driving. The team is international and combines expertise in control, optimization, and statistical inference. A