Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
resolution, across a range of research applications. These projects will be conducted in close collaboration with NGI Uppsala, providing access to state-of-the-art technologies and instrumentation for high
-
of the project, through development of the R package treepplr (www.github.com/treeppl/treepplr ). The work will include development and implementation of methods to test model adequacy, inference diagnostics
-
experimental platforms will be tailored to the applicant’s expertise and aligned with the project's needs. Working with human skin biopsies, wound tissue, and in vivo models of impaired wound healing
-
. About the position The successful candidate will do research on how to improve precision and accuracy in forest soil carbon stock estimates within the project “A better check on soil carbon - a novel
-
(e.g., two-photon microscopy). Exact experimental platforms will be decided depending on the expertise and interest of the applicant and the needs in the project. Work with human skin explants and/or
-
, or public health data from pathogen surveillance efforts and biobanks. Project description DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods, and artificial intelligence
-
biobanks. Project description DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods, and artificial intelligence to study biological systems and processes at all levels, from
-
biobanks. Project description DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods, and artificial intelligence to study biological systems and processes at all levels, from
-
). [Optional, but beneficial] A 1-2 page research proposal outlining your research questions, the biological system of focus, and experimental design. [Optional, but beneficial] A previously submitted grant
-
to understanding the evolution and dynamics of single and binary stars, particularly neutron star binaries which are detectable by LISA. A central challenge of analysing LISA data is to deal with the so-called