Sort by
Refine Your Search
-
the Radiological Image Analysis research group, we specialize in advanced image analysis methods for research applications related to metabolic and cardiovascular disease as well as cancer. The group members have
-
of Information Technology website . The project will be led by Professor Carolina Wählby , within the Image Analysis unit of the department’s Vi3 division, working alongside researchers developing numerical and
-
Physics (with focus on machine learning and bone microscopy analysis, Soft Matter Lab) The Department of Physics at the University of Gothenburg is located in the center of Gothenburg, with approximately
-
experiences, and investigative assignments are included. Relationship-building between the scientific and infrastructure teams of the program and internal communication are key elements, as is an oversight
-
and bioinformatic analysis of gene expression data in different types of tissues: mice, humans and plants. This work will include analysis of data generated with the latest Spatial Transcriptomics and
-
, from industry, academia, and/or healthcare, is a very important factor in judging candidates, preferably three years or more. It is a strong merit to have experience from operating infrastructure
-
factor – DNA binding, detecting protein – protein interactions or enzyme optimization. Main responsibilities The candidate will use and develop methods within one, or preferably multiple, of the following
-
to study host-microbiome interactions at the spatial level in the colon. The research activities of the doctoral student will focus on the experimental and computational analysis of spatial gene expression
-
. The candidate will combine experimental and computational approaches. The project will start with bioinformatics-driven analysis, followed by integration of data generated from experimental models. Over time, the
-
genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis. This project aims to use Variational Inference (VI) methods, enhanced by AI