Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Linköping University
- Uppsala universitet
- Umeå University
- Chalmers University of Techonology
- Fureho AB
- KTH Royal Institute of Technology
- Lulea University of Technology
- Lunds universitet
- Mid Sweden University
- Stockholms universitet
- Swedish University of Agricultural Sciences
- University of Lund
- 3 more »
- « less
-
Field
-
Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
-
/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
-
material layers that can be optimized to specific battery chemistries and flow phenomena from the microscale up. The developed technologies will be validated in half-cells and full working batteries
-
affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
-
of multivalent nanoparticle vaccines. The team was recently awarded an ERC Advanced Grant to determine the optimal combination of epitopes that elicits the highest level of protection. Within
-
Advanced Grant to determine the optimal combination of epitopes that elicits the highest level of protection. Within the research group, we value a positive work environment built on respect and
-
. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
-
learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group
-
. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
-
another way. The candidate must possess experience with the following technologies: - CUT&RUN - qRT-PCR and - Extensive DNA cloning technologies - Experience in Codon optimization from Dictyostelium