Available Postdoctoral Positions
(1) Postdoctoral Position in Genetic Epidemiology with Peter Kraft
The Program in Molecular Epidemiology and Statistical Genetics (PGSG) at the Harvard School of Public Health (HSPH) has an opening for a postdoctoral fellow an interest in the analysis of next generation sequencing data and the genetic epidemiology of complex traits. The primary focus of this position will be the analysis of whole genome sequencing studies of early-onset breast cancer and other traits. Other opportunities include analysis of large-scale discovery and fine-mapping studies of multiple cancers and methods development. The fellow will work closely with Dr. Peter Kraft with opportunities to collaborate with other faculty and fellows in PGSG and at HSPH.
The PGSG includes eleven faculty and research associates and thirteen postdoctoral fellows with interests in genetic and molecular epidemiology. Fellows have the opportunity to join the interdepartmental Program in Quantitative Genomics and collaborate with computer scientists, cancer epidemiologists, clinicians and biologists at HSPH and the Dana-Farber Cancer Institute. Fellows will also have the opportunity to participate in international collaborations, including the NCI’s post-GWAS Genetic Associations and Mechanisms in Oncology initiative.
This position is funded for two years.
Applicants should have a doctoral degree in a relevant field, such as genetics, epidemiology, or biostatistics. Strong quantitative and programming skills are required. Familiarity with software for managing and analyzing variant calls from sequencing data is preferred, but not necessary.
To apply, please send a cover letter, CV, and contact information for three references by email to: email@example.com. Please include “[PGSG NGS Postdoc]” in the subject line. Applications will be considered as they arrive.
(2) Postdoctoral Opportunities in Statistical Genetics with Liming Liang
The Liang lab of statistical genetics at Harvard School of Public Health invites applicants to a 2-3 year Postdoctoral Fellow position emphasizing the statistical designs and analysis of genome-wide studies for human complex diseases and traits. Our group focuses on
(1) Genetics of gene expression and methylation data, particular eQTL/meQTL mapping using both microarray and sequencing data from multiple tissues. Current studies include >2000 expression/methylation samples from multiple tissues and platforms, and samples from the NIH GTEx program.
(2) Biological network estimation using high throughput omic experiment (gene expression/methylation/metabolite) and its association with disease and trait of interest. We are particular interested in developing useful statistical model to maximize the utility of multiple related phenotypes and elucidate how genetic and epigenetic variants and their interaction with environmental factors involve in pathogenesis of complex diseases and traits. Current studies focus on Asthma, Lung Cancer, Diabetes and CVD with study subjects from both European and Asian populations.
(3) Statistical model to integrate functional data (eQTL/meQTL/metabolite QTL) with genome-wide association studies to improve power for disease variant discovery and risk prediction.
(4) New statistical approaches for the analysis of rare variants from next-generation sequencing data and exome SNP array. Ongoing projects include high depth sequencing on ~4000 breast cancer case-control subjects from multiple populations, ~6000 T2D case-control samples and >8000 CHD case-control samples with exome SNP data. The goals include fine mapping causal loci responsible for the disease and developing optimal design for next-generation sequencing studies.
The fellows will work closely with Dr. Liang, with other quantitative Ph.D’s in his group and the Program in Genetic Epidemiology and Statistical Genetics, and with collaborators at HSPH, HMS and the Broad Institute. Fellows will be mentored to facilitate transition to independent research careers by emphasizing acquisition of analytic, writing, and other research skills. Review of applications begins immediately.
Applicants should have a doctoral degree in Statistics/Biostatistics, Epidemiology, Bioinformatics, Computer Science or other relevant discipline with strong quantitative research background; practical experience working with large scale genetic data sets, developing new methods, and producing high-quality published work, are desirable.
Please submit a brief statement of interest, CV, contact information for at least 3 references, and one sample publication by email to Liming Liang, firstname.lastname@example.org. Address: Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Building 2, Room 211A, 655 Huntington Ave, Boston, Massachusetts 02115. Phone: 617.432.5896. HSPH is an Equal Opportunity/Affirmative Action Employer. Women and minorities are encouraged to apply.
(3) Postdoctoral Opportunities in Statistical Genetics with Alkes Price
September 1, 2013: A post-doctoral position is available in the statistical genetics research group of Dr. Alkes Price, a faculty member at the Harvard School of Public Health. The fellow will work closely with Dr. Price, with other quantitative Ph.D’s in his group, and with collaborators at HSPH, HMS and The Broad Institute. Questions that we aim to answer include: (1) Which association scoring statistics provide maximum power to identify disease genes in admixed or structured populations, accounting for admixture association, imputation and fine-mapping, family relationships, and advantages of mixed model methodology, (2) What is the contribution of different classes of genetic variation to the heritability of quantitative and case-control traits and how does this inform polygenic prediction methods, and (3) Which new statistical approaches will maximize the informativeness of resequencing data for identifying disease genes in homogeneous, structured and/or admixed populations.
Exceedingly strong quantitative research background; practical experience working with large real-world genetic data sets, developing new methods, and producing high-quality published work. Preference will be given to candidates with degrees in computer science or other applied quantitative fields.