Sort by
Refine Your Search
-
services can be found at: https://www.uu.se/forskning/snpseq and https://ngisweden.scilifelab.se/ We are proud to deliver high-quality data and are accredited by SWEDAC as a testing laboratory under the ISO
-
of Medical Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research
-
visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala Form of employment: Temporary employment 24 months, with the possibility of extension. Scope: 100% Start date: 1st of September 2026
-
. The research environment is embedded in Linköping University and closely connected to SciLifeLab and the national DDLS program. https://liu.se/en/employee/bjofo78 https://www.nsc.liu.se/systems
-
negotiated salary progression. More information about employee benefits is available https://liu.se/en/work-at-liu/employee-benefits . Union representatives Information about union representatives, see https
-
of the SciLifeLab Integrated structural biology platform https://www.scilifelab.se/units/structural-proteomics/ The unit provides access to cutting-edge equipment and expertise, for the analysis
-
. Location: SciLifeLab , Solna https://ki.se/en/research/research-areas-centres-and-networks/research-groups/uncovering-the-molecular-and-physical-principles-governing-early-embryonic-division-and-nuclear
-
of proteomics and MS experiments in close collaboration with platform users and the team members, as well as sharing knowledge within the facility team. You will take part in multiple projects applied
-
technical staff. Research group website: https://johannescairns.github.io/meg-lab/ . We offer Lund University is a government agency, which means you receive special benefits, generous vacation, and a
-
). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep