Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Lunds universitet
- SciLifeLab
- Umeå University
- Luleå tekniska universitet
- Mälardalen University
- Swedish University of Agricultural Sciences
- University of Lund
- Karlstad University
- Linköping University
- Blekinge Institute of Technology
- Institutionen för biologi och miljövetenskap
- Sveriges Lantbruksuniversitet
- Umeå universitet stipendiemodul
- University of Borås
- Uppsala University
- Uppsala universitet
- universitypositions
- 9 more »
- « less
-
Field
-
roles in academia, industry, or the public sector. Contract terms The position is a temporary full-time employment for two years with the possibility of a one-year extension. The position requires
-
employment for two years with the possibility of a one-year extension. The position requires physical presence throughout the entire employment. A valid residence permit must be presented by the start date
-
regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
-
also includes routine tasks associated with the day-to-day operation of the laboratory. Qualifications For the position, you are required to: have a strong ability to work independently and in a
-
at: www.ftf.lth.se , www.nano.lu.se , https://kaw.wallenberg.org/en/research/semiconductor-bandgap-key-future-green-electronics, https://c3nit.se/ Subject description The purpose of this project is to develop a data
-
120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. The department also participates in the Wallenberg AI, Autonomous
-
employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the
-
regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
-
on various aspects along the battery value chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoc project Atomistic modelling and synthesis
-
, modeling to scaled-up manufacturing. The 2-year postdoc project Electrochemical properties of solid-state batteries and analysis of interactions between its different components will be shared between Depts