14 data-"https:" "https:" "https:" "https:" "New York University" research jobs at SciLifeLab
Sort by
Refine Your Search
-
Listed
-
Program
-
Field
-
on bioinformatics analysis of spatial gene expression data as well as other modalities (i.e. microbiome; metabolites, proteins) generated using the Spatial Transcriptomics (ST) method, Spatial metaTranscriptomics
-
: October 2026 Full call details, eligibility criteria, application templates, and a matchmaking platform for identifying potential supervisors are available at: https://www.scilifelab.se/data-driven/ddls
-
. Salary and employment benefits The university applies individual salaries. More information about employee benefits is available https://liu.se/en/work-at-liu/employee-benefits . Union representatives
-
or senior staff scientist. More information: https://www.scilifelab.se/researchers/simon-koplev/ Qualifications Requirements A doctoral degree or an equivalent foreign degree. This eligibility requirement
-
and evolutionary dynamics. The positions are based in Assistant Professor Lisandro Milocco’s research group at Stockholm University and SciLifeLab, within Sweden’s national Data-Driven Life Science
-
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
-
entitles you to several benefits through our collective agreement. Location: Solna More information https://bensonlab.se/ Application An employment application must contain the following documents in English
-
. Active participation in major international consortia. Support for career development, conference travel, and long-term academic progression More information: https://www.scilifelab.se/researchers/simon
-
the lab and bioinformatic/statistical analyses of next-generation sequence data. The project will be conducted in collaboration with Prof. Göran Arnqvist (Evolutionary Biology Centre, Uppsala University
-
). 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