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Senior Statistical Geneticist
We're looking for an analytical thinker with a technical background working in human genetics or genomics to join our research team as a Senior Statistical Geneticist. This role will design and implement genetics and genomics research focusing on investigation of human genome function in healthy and disease cohorts.
You will join a team working on disentanglement of the relationship between genotype and phenotype to better understand the forces shaping functional genetic variation in humans. We use a combination of experimental and computational approaches: We employ high-throughput functional genomics and genome-wide association analysis (GWAS) to identify variable regions of the genome with functional effects on the transcriptome and proteome, and to investigate the context-specificity of these patterns. At the same time, we examine the levels and patterns of genetic variation within and between human populations, and between human and other species to identify regions of the genome with patterns of variation suggestive of natural selection.
Key Responsibilities
The position is grant supported and longevity of the position is dependent upon future funding. This position is a joint position between Dr. Barbara Stranger's laboratory and Dr. Robert Grossman's laboratory in the Center for Data Intensive Sciences, the Institute for Genomics and Systems Biology, and the Section of Genetic Medicine at the University of Chicago.
Required Qualifications
Preferred Qualifications
How to Apply
Apply under Requisition#101565 at jobopportunities.uchicago.edu
About the Center for Data Intensive Science
The Center for Data Intensive Science at the University of Chicago is developing the emerging field of data science with a focus on applications to problems in biology, medicine, and health care. Our vision is a world in which researchers have ready access to the data and tools required to make discoveries that lead to deeper understanding and improved quality of life. We democratize access, speed discovery, create new knowledge and foster innovation through implementation using data at scale. Our data sharing technology powers several scientific data clouds and commons including the Genomic Data Commons, Blood Profiling Atlas, INRG Data Commons, Bionimbus Protected Data Cloud, and Open Science Data Cloud.
About the Institute for Genomics and Systems Biology
Founded in 2006, IGSB has grown to nine core members with over 50 students and staff, as well as over fifty participating faculty Fellows who are making basic discoveries in the biological sciences. Topics pursued by IGSB investigators are very diverse. A sampling includes: the genetic mechanisms of cancer, the annotation of the human genome, the molecular networks that control development, the role of microbes in the planet’s carbon cycle, mining clinical records to identify disease interactions, mapping signaling networks in human cells, identification of lead molecules for a dozen different diseases, and many other complex biological problems that require systems-wide and genomic information to decode.
About the Section of Genetic Medicine
The Section of Genetic Medicine was created over 10 years ago to both build research infrastructure in genetics within the Department of Medicine and to focus translational efforts related to genetics. As a result, the Section of Genetic Medicine is shaping the future of precision medicine with very active and successful research programs focused on the quantitative genetics, systems biology and genomics, and bioinformatics and computational biology. The Section provides extremely valuable collaborations with investigators in the Department of Medicine who are seeking to develop new and more powerful ways to identify genetic risk factors for common, complex disorders with almost immediate clinical application.
We're looking for an analytical thinker with a technical background working in human genetics or genomics to join our research team as a Senior Statistical Geneticist. This role will design and implement genetics and genomics research focusing on investigation of human genome function in healthy and disease cohorts.
You will join a team working on disentanglement of the relationship between genotype and phenotype to better understand the forces shaping functional genetic variation in humans. We use a combination of experimental and computational approaches: We employ high-throughput functional genomics and genome-wide association analysis (GWAS) to identify variable regions of the genome with functional effects on the transcriptome and proteome, and to investigate the context-specificity of these patterns. At the same time, we examine the levels and patterns of genetic variation within and between human populations, and between human and other species to identify regions of the genome with patterns of variation suggestive of natural selection.
Key Responsibilities
- Design and implement genetics and genomics research that focuses on investigation of human genome function in healthy and disease cohorts
- Perform data processing and quality control for genetic and genomic data
- Perform integrative statistical analysis of genome-wide genomic datasets.
- Write and implement efficient and effective code and pipelines for data processing, quality control, and analysis.
- Download and organize data repositories, manage data access, and design and manage internal databases as needed.
- Recognize poor quality data, abnormal results, and troubleshoot analytic pipelines.
- Adapt existing tools and pipelines to current work needs and make significant contributions to analysis methodology.
- Ensure that projects are managed in a timely and efficient manner, and that all projects are appropriately documented
- Summarize results, create high quality graphics, and interpret findings for presentations and publication, and write scientific manuscripts.
- Assist in supervising and training laboratory trainees in statistical genetics, bioinformatics, and computation
- Assist in grant writing where appropriate
- Coordinate work with other scientists in both the Stranger laboratory and Center for Data Intensive Science, and collaborate effectively on joint research projects internally and with external and consortia collaborators
The position is grant supported and longevity of the position is dependent upon future funding. This position is a joint position between Dr. Barbara Stranger's laboratory and Dr. Robert Grossman's laboratory in the Center for Data Intensive Sciences, the Institute for Genomics and Systems Biology, and the Section of Genetic Medicine at the University of Chicago.
Required Qualifications
- PhD degree (or Masters + 3 years experience) in genetics, statistical genetics, bioinformatics, computational biology, population genetics, or related field required
- A minimum of three (3) years experience in statistical genetics, computational biology, or bioinformatics required.
- Experience within one of the following areas is required: human genetics, genomics, and statistical interpretation and modern analysis methods for next-generation genomic data.
- Experience using languages such as Java, SQL, XML, with C/C++, Perl, Python, R or PHP required.
- Experience with Linux and/or Unix required.
- Experience in employing high performance computing (both cluster and cloud based) to solve parallelizable compute problems required.
Preferred Qualifications
- Experience providing bioinformatics services or other service roles preferred.
- Experience with cancer genetics preferred.
- Supervisory experience preferred.
- Experience in providing bioinformatics training preferred.
- Experience in project management preferred.
- Experience in analysis of DNA-seq, RNA-seq, and/or CHiP-seq data, as well as experience constructing complex biological networks preferred.
- Experience teaching bioinformatics to non-bioinformaticians preferred.
How to Apply
Apply under Requisition#101565 at jobopportunities.uchicago.edu
About the Center for Data Intensive Science
The Center for Data Intensive Science at the University of Chicago is developing the emerging field of data science with a focus on applications to problems in biology, medicine, and health care. Our vision is a world in which researchers have ready access to the data and tools required to make discoveries that lead to deeper understanding and improved quality of life. We democratize access, speed discovery, create new knowledge and foster innovation through implementation using data at scale. Our data sharing technology powers several scientific data clouds and commons including the Genomic Data Commons, Blood Profiling Atlas, INRG Data Commons, Bionimbus Protected Data Cloud, and Open Science Data Cloud.
About the Institute for Genomics and Systems Biology
Founded in 2006, IGSB has grown to nine core members with over 50 students and staff, as well as over fifty participating faculty Fellows who are making basic discoveries in the biological sciences. Topics pursued by IGSB investigators are very diverse. A sampling includes: the genetic mechanisms of cancer, the annotation of the human genome, the molecular networks that control development, the role of microbes in the planet’s carbon cycle, mining clinical records to identify disease interactions, mapping signaling networks in human cells, identification of lead molecules for a dozen different diseases, and many other complex biological problems that require systems-wide and genomic information to decode.
About the Section of Genetic Medicine
The Section of Genetic Medicine was created over 10 years ago to both build research infrastructure in genetics within the Department of Medicine and to focus translational efforts related to genetics. As a result, the Section of Genetic Medicine is shaping the future of precision medicine with very active and successful research programs focused on the quantitative genetics, systems biology and genomics, and bioinformatics and computational biology. The Section provides extremely valuable collaborations with investigators in the Department of Medicine who are seeking to develop new and more powerful ways to identify genetic risk factors for common, complex disorders with almost immediate clinical application.