Postdoctoral Fellow in Quantitative Biology and Longevity Research

Job Description:
Dr. Nicholas Schork, Director of Quantitative Medicine at TGen, seeks a creative, independent, and highly motivated researcher interested in developing and applying novel and comprehensive quantitative methods for understanding and characterizing the genetic and molecular determinants of human longevity and longevity enhancement. Dr. Schork is one of the two principal investigators (PIs) for the NIA-funded longevity consortium, whose goal is to characterize the genetic basis of human longevity, and a co-PI on another NIA-funded consortium grant to identify genetic targets for longevity-enhancing interventions. The methods, analyses, and tools to be developed will leverage these connections, but go beyond them, in developing bioinformatic and biostatical analysis methods to identify genetic longevity-enhancing intervention targets and designing studies to test their effects.

Detailed Description

Brief Professional Activities Description:

The post-doctoral fellow will have the opportunity to develop and evaluate quantitative methods for interrogating genetically-mediated factors that impact human longevity and leverage them in the design and analysis of longevity-enhancing intervention studies. TGen provides a very collaborative environment that includes basic scientists, computational and quantitative scientists, biomedical technology developers and clinicians, including those at TGen's affiliated institution, the City of Hope Hospital in East Los Angeles, as well as its many other partner institutions.

There is tremendous interest in the identification of factors that affect human longevity and potential interventions that enhance human longevity. Unfortunately, human longevity is a complex, multifactorial phenotype with a number of interacting genetic and non-genetic determinants. Characterizing the factors influencing longevity will be difficult since the effect of any one factor can be obscured by the effects of others. As a result, more sophisticated and integrated approaches are needed. This is true for both the identification and characterization of longevity-enhancing factors and testing longevity-enhancing interventions that target these factors. A research challenge will be to develop reliable techniques for mining data relevant to genetically-mediated factors (for example from genome-wide association studies focusing on longevity) to identify better longevity-enhancing intervention targets, as well as methods to test interventions that exploit those targets. In this context, the proposed activity and research will involve:

• Modeling and the influence of genetic variants on human longevity using imputation and mediation tests
• Developing strategies for identifying drugs and nutritional interventions that beneficially modulate longevity-associated factors
• Designing studies to test interventions that target longevity-enhancing factors
• Designing studies that measure factors that indicate changes consistent with a health benefit
• Helping oversee the conduct of studies to identify and test factors that enhance longevity
• Present findings on longevity enhancement at major conferences and in peer-reviewed publications

TGen has access to resources (such as some of the largest and fastest computing systems in the world, a large phase I clinical trial center, a CLIA lab, genomic and other omic assay-oriented labs) that can be leveraged in the proposed research. In addition, the development of the proposed strategies and methods will likely have a broad, international impact, given the amount of attention human longeveity research is receiving, so the research to be pursued will be highly visible and competitive, and as a result could propel the career of an independently-minded researcher looking to make an indelible impact on a biomedical science and humanity in general.

Job Requirements:

• Ph.D. (completed or near completion) in Bioinformatics, Systems Biology, Computational or Quantitative Biology, Biostatistics, Statistics, Applied Mathematics, or a related field.
• Computer programming, R and other statistical analysis packages, and scripting languages skills
• Experience in developing linear and non-linear statistical models
• An understanding of genetics and genomics
• Experience with large biological data sets
• Excellent written and verbal communication skills
• Willingness to work in collaborative settings
• No publication record is required, although a publication track record is preferred

---

Requeriments :

• Doctorate Degree

Skills :

• Genetics
• genomic
• Modeling
• Programing Skills
• Programming in R
• Statistics

Areas :

• Bioinformatics

---

Additional Info:
Please submit your CV, a statement about your research interests and experience, and the names of three references.

Currency : USD

[Click Here to Access the Original Job Post]