Machine learning/computational biology post-doctoral fellow position available July 1, 2016 in the research lab of Dr. Georg Gerber (http://gerber.bwh.harvard.edu)at the Massachusetts Host-Microbiome Center at Harvard Medical School.
The Gerber lab develops novel Bayesian statistical/machine learning models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. A particular focus of the Gerber lab is understanding dynamic behaviors of host-microbial ecosystems. Our work in this area includes the MCTIMME and MDSINE algorithms, for discovering temporal patterns in microbiome data or for inferring dynamical systems models from microbiome time-series data. We have applied these methods to a number of clinically relevant questions including understanding dynamic effects of antibiotics, infections and dietary changes on the microbiome, and designing bacteriotherapies for Clostridium difficile infection and inflammatory bowel disease.
Qualifications:
- PhD in computer science, applied mathematics, statistics, or other quantitative discipline from top institution
- Strong track record developing novel machine learning/statistical models using Bayesian methods
- Curiosity about biology/medical applications and some experience modeling biological systems; microbiome experience desirable, but not required
- Excellent publication track record
- Superior communication skills and ability to work on multidisciplinary teams
Send cover letter and CV to This email address is being protected from spambots. You need JavaScript enabled to view it.
Please take the time to look over some publications from the Gerber lab and to write a relevant cover letter; applications without a cover letter specifically responsive to this posting will not be considered.
