The goal of this project is to develop computational algorithms and methods that use omics data
to infer gene regulatory networks (GRNs), and apply these to understand regulatory mechanisms
that lead to cancer formation. Cancer cells are subjected to scRNA-seq to measure the
transcriptomic response of gene knockdown perturbations via CRISPR interference on a large
scale. The Sonnhammer group has recently shown that such data data offers a significant
improvement in GRN quality compared to non-perturbed data.
The project involves developing and applying computational methods that can work optimally
with this new type of data, either by adapting and optimizing existing methods, or designing new
methods based on deep learning neural networks and variational autoencoders. Also new methods
for quality assessment need to be developed. The resulting predicted regulatory mechanisms will
be forwarded for evaluating their clinical relevance.
The successful candidate must be highly motivated and have an M.Sc. in bioinformatics or
related field, and knowledge of molecular biology. Alternatively, an M.Sc. in molecular biology
or related field and at least 1 year of documented practical experience in bioinformatics research
and programming. Demonstrable familiarity with sequence and molecular data analysis
techniques is essential. Extensive experience with Python, Matlab, and R, and good UNIX
knowledge are necessary skills.
Deadline April 23.
See sonnhammer.org/download/ads/open.html for application system