The Karlsson Lab in the Program Bioinformatics and Integrative Biology at the University of Massachusetts Medical School is looking for an exceptional postdoctoral candidate to lead our work developing innovative new computational methods for studying human evolution in collaboration with scientists at the Broad Institute. The Karlsson lab uses the distinctive patterns left by ancient evolutionary events to investigate how our immune system combats infectious diseases, and how we can improve treatments and vaccines for diseases, like cholera, that affect millions of people every year.
Computational postdoctoral fellow who can conceive and develop algorithms and analysis approaches for integrating diverse types of data and identifying functionally important genes and regions. A highly competitive salary and excellent benefits package will be provided commensurate with experience.
- Devise new algorithms and approaches to analyze natural selection, association and other types of whole genome data.
- Test methods on simulated data modeling a range of human population histories
- Work closely with experimentalists to validate methods using real genomic datasets
- Optimize successful algorithms for use by the broader scientific community
This position is an opportunity for experienced computer scientists to work at the cutting edge of medical genomics. The ideal candidate will have a strong quantitative research background and practical experience working with large, complex data sets, developing new analysis methods, and producing high quality published work. Experience in machine learning, signal processing and/or data mining is preferred. A background in human genetics and computational biology is helpful, but not required.
The candidate will also have shown the ability to solve complex problems individually and as part of a team; have excellent oral and written English communication skill; and have experience developing software in one or more programming languages.
Preference will be given to candidates with degrees in computer science, bioinformatics, statistical genetics or other applied quantitative fields.