My lab is developing (i) novel cancer therapeutics based on the normal signaling of the epithelial-stromal program and (ii) computational methods for identifying biomarkers of tumor heterogeneity.
Parvin's Lab, at LBNL, received an R&D100 award in 2014 for the development of BioSig3D. BioSig3D is the first system for high content screening of 3D cell culture models.
- Bioimaging, Tissue Engineering, Deep Learning, and DSP
Prospective graduate students
There are open positions for highly motivated graduate students interested in cancer biology or bioimaging and machine learning.
- Cancer biology: Graduate students will investigate the mechanism of tumor suppression using cell-based assays, immunostaining, and OMIC data. There will also be future opportunities in drug delivery and animal studies. Applicants must have a background in molecular biology, biochemistry, or bioengineering. A B.S. in Engineering is not required.
- Bioimaging and machine learning: Graduate students will develop novel algorithms and software to identify biomarkers of tumor response based on multiplexed spatial and high dimensional discrete data. Applicants must have a master’s degree in computer science or engineering with solid programming skills in Python or related languages.
Interested applicants should email Dr. Bahram Parvin with the subject heading Prospective Graduate student (Cancer biology or Machine Learning) and include a CV outlining research interests, skills, and a list of references.