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 Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo.
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.
