We are looking for a motivated, independent post-doctoral and team-oriented researcher in AI and machine learning eager to work on the development of new methodologies and computing solutions to analyze complex, high-dimensional data. The project involves both real-world data analysis and new methods development. The key analytical challenge in this project is to analyze a new type of massively parallel gene knockout experiments in the context of brain tumor studies. Towards this goal, you will develop methods for neural network regularization appropriate for the analysis of noisy data and algorithms for optimal experimental design, as well as data augmentation and transfer learning methods to combine heterogeneous data sources.

Information about the research project

The announced post-doc 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.

This position is part of a twin-post-doctoral research project “Massively parallel in vivo gene editing and AI modeling to decipher brain tumor invasion”. This post-doctoral researcher will join Rebecka Jörnsten’s group at Chalmers and the DDLS post-doctoral researcher will join the Nelander lab at Uppsala University. Rebecka Jörnsten’s research group at the Division of Applied Mathematics and Statistics comprises 4 PhD students. The research group is focused on network modeling, data integration and regularization techniques in high-dimensional statistical modeling and neural networks. We collaborate with several research groups in cancer genomics and bioinformatics.

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