We are looking for a highly-motivated postdoctoral fellow to work with Prof. Sriram Sankararaman in the Departments of Computer Science and Human Genetics at UCLA. We have broad research interests: ranging from statistical inference and machine learning algorithms and application of these tools to problems in medical and population genomics. We develop statistical model and machine learning algorithms to understand evolutionary processes as well as genetics of complex traits and in applying these methods to large-scale datasets to answer biological questions.
An ideal candidate will have strong background in statistics and computation, programming skills, and population genetics with an ability to perform independent research. Potential projects include, but are not limited to:
1. Computational methods to study admixture at diverse time-scales and its impact on phenotype.
2. Methods for analyzing genomic time series.
3. Inferring demographic history from genome sequences.
4. Methods for genetic analysis of complex traits.
5. Algorithmic and inferential challenges that arise in these problems.
Our lab is tightly integrated with a vibrant research community in bioinformatics, population and medical genetics at UCLA. We collaborate closely with many other labs in computer science (Eskin, Halperin), human genetics (Pasaniuc, Freimer, Flint, Sul) and population genetics (Lohmueller). Further, we are part of the Institute for Quantitative and Computational Biosciences at UCLA (qcb.ucla.edu).
Candidates must hold a Ph.D. in computer science, statistics, bioinformatics, biostatistics, computational biology, or a related quantitative field and have a strong publication record. The position is available for 1 year and may be continued for an additional year contingent on successful progress and available funding. The expected start date is fall 2017 but is flexible.
Interested candidates should email Sriram Sankararaman (sriram@cs.ucla.edu) with their CVs, research statements and names of references.