The project aims to develop machine and deep learning models for genomic data analysis (SNV, CNV, etc.) obtained through whole-exome or whole-genome DNA sequencing and functional analysis of the results. Developed tools will leverage genetic data to predict when a specific cancer might develop, offering personalized insights into germline genomic contributions to cancer risk. It may also serve as a foundation for integrated scores considering both genomic and environmental factors, helping identify individuals with increased cancer development risks.
The ideal candidate should possess a PhD in computational biology, bioinformatics, data science, or a related quantitative discipline, along with exceptional programming skills in Python and R. Proficiency in analyzing high-throughput sequencing data and developing deep-learning models is highly desirable. The selected individuals will join a team of bioinformaticians/data scientists and have the opportunity to collaborate with oncologists from Yale University, potentially involving short visits to Yale.
Please send your CV to michal.marczyk@polsl.pl