Postdoc position in Information Visualization and Visual Analytics at the Department of Science and Technology, Campus Norrköping, Linköping University.
The advertised position will be affiliated to the Information Visualization research unit (iVis) led by Professor Andreas Kerren (see https://liu.se/en/research/ivis). The research group mainly focuses on the explorative analysis and visualization of typically large and complex information spaces, for example in environmental research, transportation systems, social sciences, or artificial intelligence. Our vision is to attack the big data challenge by a combination of human-centered data analysis and interactive visualization for deriving meaning from the data and final decision making. Our research is highly relevant for academia and industry as both make increasing use of data-intensive technologies.
This position is part of a joint collaboration between the two largest research programs in Sweden, the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), with the ultimate goal of solving ground-breaking research questions across disciplines.
The collaboration between WASP and DDLS will be performed in the context of an interdisciplinary research project entitled “Visual Analytics for Enhancing Quality and Trust in Genome-wide Expression Clustering and Annotation”, where the Systems Biology group at the Royal Institute of Technology (KTH) and the Information Visualization group at Linköping University (LiU) work closely together.
There is a need for a functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. The new data-driven strategy to be developed in the project is based on interpretable unsupervised learning (e.g., dimensionality reduction and clustering) of whole-body co-expression patterns, supported by state-of-the-art visual analytics. Interactive guiding of the clustering process will be used to explore the gene expression landscape in humans and other mammalian species, and to create a whole-body map of all protein-coding genes in all major cell types, tissues and organs. This will allow for the improvement of the quality of the classification of all protein-coding genes according to their whole-body co-expression patterns, resulting in every gene being annotated to a unique expression cluster together with other genes with a similar body-wide pattern.
The focus of this advertised Postdoc position at LiU will be on research and development of an interactive and interpretable (human-in-the-loop) clustering strategy to reach these goals. The successful candidate will develop visual analytics approaches that provide fine-grained quality analysis of the clustering processes and better interpretation/annotation of clusters.