We are currently accepting applications for computational biology postdoctoral fellows to undertake several exciting projects. This opportunity requires developing computational algorithms and machine learning methods to solve biological problems. We are especially interested in developing algorithms to study genetic causes of complex disorders such as autism, intellectual disability, epilepsy and cancer. The technical expertise needed include strong computational background to develop novel combinatorial, machine learning or statistical inference algorithms and a general understanding of the concepts in genomics and genetics.
Candidates are guaranteed funding for two years and will be strongly encouraged to apply for external funding in the second year of their postdoc to make successful transition to independent investigator.
Some of the projects to work on include but are not limited to:
Algorithms for module/pathway discover in complex disorders: Many of the complex diseases display a high heterogeneity, which means there are many genes contributing to the disorder. The goal of this project is to utilize the combination of available biological data such as protein-protein interaction (PPI) networks, coexpression networks and other relevant biological data to find functionally coherent modules (i.e. "highly" connected subnetworks) which are significantly mutated in the complex disease being studied.
Algorithms for complex and neglected variant discovery using whole-genome sequencing (WGS): It was recently been shown that the current methods for finding structural variation and complex variants show a high false negative rate. In addition the current studies of complex disorders (such as autism and cancer) tend to only focus on variants that are easy to find (SNV and large size CNV). We work on developing novel computational algorithm which are capable of discovering all types of variants with higher recall than before. We will further use these novel methods to analysis WGS data from samples with complex disorders.
Classification and early prediction of diseases using combination of rare and common variants: One of the main goals of precision medicine is capability of early prediction of diseases. We are extensively working on developing novel machine learning algorithms to predict diseases such autism and cancer using combination of rare and common variants.
The candidates require these skills:
- PhD in computer science, computational biology or related fields
- Excellent programming skills in at least one language (C/C++, Java or Python)
- Strong written/oral presentation skills
- Be enthusiastic to work on complex disorders and/or genomics
Interested in candidates can send their CV to Fereydoun Hormozdiari (email: fhormozd[at]ucdavis.edu).