Our project group is working to design and build a machine learning-driven autonomous system for genetic engineering of novel functionality into microbial systems. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position requires expertise in Computer Science, Statistics, or a similar field. Experience with machine learning, genetics, and/or bio-informatics is strongly preferred. 

The postdoc will work together and within a collaborative, interdisciplinary team to enable innovative methods for the predictive engineering of genetic sensors and other living measurement systems in bacteria and yeast. Facilities available for this project include state-of-the-art automation for microbial engineering, culture, and measurement.

 

 

key words

Machine Learning; Biology; Bioinformatics; Data Mining; Genetics; Active Learning

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants