This is an Industry PhD Project which is a collaboration between Stockholm University and Merck AB. The PhD student will be employed by Merck AB but registered and accepted to the PhD program at Stockholm University. The project is will have a base in the Sonnhammer group at Science for Life Laboratory in Stockholm, Sweden, which is a strong research environment for large-scale life science research, and a joint physical center for a number of computational and life science groups at Stockholm University, KTH, and Karolinska Institutet. The research project will be supervised by Professor Erik Sonnhammer and Dr. Dimitri Guala.
A new exciting opportunity is combining spatial biology with AI-driven modeling of gene expression responses to drug treatment in the field of drug repurposing. Drug repurposing involves identifying new therapeutic uses for existing medications, a strategy that can significantly reduce the time and cost required to bring a drug to market. The project will use AI models such as CycleGANs, that by learning from complex spatial gene expression profiles and cellular heterogeneity within tissues can predict how existing drugs might act on previously uncharacterized disease mechanisms or cellular subtypes. These models will be employed to translate spatial gene expression profiles from healthy tissues to disease states, and vice versa. This capability allows researchers to simulate the effects of drug treatments on spatially resolved gene expression without the need for extensive experimental data. By learning the underlying mappings between these domains, synthetic data will be generated that reflects potential drug responses, thereby enhancing the predictive power of our models.
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. Extensive experience with Python, deep learning techniques, and good UNIX knowledge are essential skills. Knowledge of Matlab, R, bash scripting and familiarity with biological omics data analysis techniques are desirable merits.
See careers.merckgroup.com/global/en/job/288421/Industrial-PhD-student-in-Bioinformatics