Job Description:
Post-doc in Machine Learning/Computational BiologyHarvard Medical School/Brigham and Women's HospitalCenter for Clinical and Translational MetagenomicsMA-Boston Machine learning/computational biology postdoctoral fellow needed to work in the Center for Clinical and Translational Metagenomics at Harvard Medical School in the research group of Dr. Georg Gerber (http://gerber.bwh.harvard.edu). The successful applicant will develop and apply novel statistical/machine learning methods to:
- Infer dynamic behaviors of the microbiota in human subjects and experimental animal systems
- Infer microbe-microbe and host-microbe interaction networks in natural and synthetic biology systems
- Predict host phenotypes, including disease status in patients, from static or longitudinal microbiome and host immune system data
Qualifications:
- PhD in computer science, applied mathematics, statistics, or other highly quantitative discipline from top institution
- Previous experience performing high-quality machine learning/statistical research using Bayesian methods
- Strong mathematical abilities with track record creating novel models and inference algorithms
- Some previous experience modeling biological systems (microbiome experience desirable, but not required)
- Experience implementing and running algorithms in high-performance parallel computing environments
- Excellent publication track record
- Excellent ability to communicate complex ideas and work on multidisciplinary teams with others not versed in machine learning/statistical methods
Job Information Position Type: Postdoctoral Research Fellow Start Date: April 15, 2015 Duration: Full Time Status: openContact InformationHarvard Medical School/Brigham and Women's Hospital
Center for Clinical and Translational Metagenomics
Georg Gerber, MD, PhD, MPH
ggerber@partners.orghttp://gerber.bwh.harvard.eduHow To Apply:E-mail CV and cover letter. Applications that do not include a cover letter responsive to the position will not be considered.
Additional Info:
[Click Here to Access the Original Job Post]
Post-doc in Machine Learning/Computational BiologyHarvard Medical School/Brigham and Women's HospitalCenter for Clinical and Translational MetagenomicsMA-Boston Machine learning/computational biology postdoctoral fellow needed to work in the Center for Clinical and Translational Metagenomics at Harvard Medical School in the research group of Dr. Georg Gerber (http://gerber.bwh.harvard.edu). The successful applicant will develop and apply novel statistical/machine learning methods to:
- Infer dynamic behaviors of the microbiota in human subjects and experimental animal systems
- Infer microbe-microbe and host-microbe interaction networks in natural and synthetic biology systems
- Predict host phenotypes, including disease status in patients, from static or longitudinal microbiome and host immune system data
Qualifications:
- PhD in computer science, applied mathematics, statistics, or other highly quantitative discipline from top institution
- Previous experience performing high-quality machine learning/statistical research using Bayesian methods
- Strong mathematical abilities with track record creating novel models and inference algorithms
- Some previous experience modeling biological systems (microbiome experience desirable, but not required)
- Experience implementing and running algorithms in high-performance parallel computing environments
- Excellent publication track record
- Excellent ability to communicate complex ideas and work on multidisciplinary teams with others not versed in machine learning/statistical methods
Job Information Position Type: Postdoctoral Research Fellow Start Date: April 15, 2015 Duration: Full Time Status: openContact InformationHarvard Medical School/Brigham and Women's Hospital
Center for Clinical and Translational Metagenomics
Georg Gerber, MD, PhD, MPH
ggerber@partners.orghttp://gerber.bwh.harvard.eduHow To Apply:E-mail CV and cover letter. Applications that do not include a cover letter responsive to the position will not be considered.
Skills :
Areas :
Additional Info:
[Click Here to Access the Original Job Post]