We seek a driven and technically adept Postdoctoral Fellow to develop and deploy innovative graph-based network representations and classifiers to enable integrative analyses of multi-scale cancer data. The primary research focus is to apply cutting-edge graph-based methods and machine learning/deep learning techniques to construct and analyze network representations of 3D-structural data toward the identification of new therapeutic leads. These efforts will inform clinical understanding here at MD Anderson and pave the way for new therapeutic strategies.

LEARNING OBJECTIVES
Develop innovative methods to advance graph-based network representations and classifiers based on 3D protein data. Build interpretable models to determine the impact of mutations and highlight key points of vulnerability in cancer networks for therapeutic translation.

ELIGIBILITY REQUIREMENTS
Individuals with a PhD degree in computer science, engineering, or the natural sciences are encouraged to apply. A strong computational background is required, including proficiency in Python, R, and UNIX. Familiarity with graph-based methods and machine learning/deep learning techniques is essential, and prior experience in cancer biology and analyzing protein or 3D structural data would be desired. Excellent communication skills, both written and verbal, and the ability to work in a multidisciplinary team are essential in this role.

ADDITIONAL APPLICATION INFORMATION
Interested applicants should upload a Cover Letter specifying past research experience/skills/accomplishments, a Curriculum Vitae, and the contact information of three (3) references