Postdoctoral research position at USC in Human Medical Population Genetics

Salary Range: starting at $68,000 -$70,000 or commensurate with experience.

When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state, and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations. or commensurate with experience.

A postdoctoral research position is available in the lab of Dr. Nicholas Mancuso in the Center for Genetic Epidemiology, Department of Population and Public Health Sciences at the University of Southern California, Keck School of Medicine. The MancusoLab aims to develop novel computational and statistical approaches to understand the genetic etiology of complex diseases. This includes integrating molecular phenotypes (e.g., gene expression, protein abundance) with large-scale genome-wide association studies, characterizing the genetic architecture of complex disease (e.g., rare vs common variation), and quantifying the role of selection in shaping the effect-size distribution for alleles.

A successful candidate will perform research in statistical genetics by developing novel computational/statistical tools in one or more of the following areas:

• Multi-ancestry analysis of genome-wide association studies (e.g., prostate cancer, complex traits, diseases),
• Multi-ancestry analysis of molecular QTL studies (e.g., eQTL, pQTL, etc)
• Single cell molQTL analyses,
• Integrative approaches combining multiple data modalities (e.g., GWAS, molQTL, MPRA, etc).

Opportunities are available to analyze large-scale genotyping and next-generation sequencing data in humans along with molecular phenotypes (e.g., mRNA/protein in prostate). Additional information about our lab and research can be found at www.mancusolab.com.

The Center for Genetic Epidemiology is closely linked with the Departments of Population and Public Health Sciences, Translational Genomics and Computational Biology and Bioinformatics. The Department of Population and Public Health Sciences is one of the nations leading research programs in epidemiology and biostatistics, with particular expertise in genetics research in diverse populations. The Departments of Translational Genomics and Computational Biology and Bioinformatics provide key technological, methodological and statistical supports of genetics research. Together, these Departments offer exceptional resources and collaborative opportunities for postdoctoral fellows. 

The position is available for 1 year and renewable contingent on successful progress and available funding. Salary will be competitive. The University of Southern California offers a competitive benefits package including medical, dental, vision, life insurance, accidental death and dismemberment insurance, and short and long term disability insurance.

Candidates should have a recent Ph.D. in biology, genetics, computer science, bioinformatics, computational biology, or a related field. Proficiency in one or more programming languages (e.g., Python, R, etc.) and in Unix-based computing environments is essential. Competitive applicants will also have extensive experience in conducting human genetics research and analyzing large genetic datasets, and the desire to apply for external fellowship funding. Preference will be given to candidates with a compelling publication record, evidence of substantial research productivity, and ability to successfully communicate scientific information.

Review of applications will begin immediately and will continue until the position is filled. The position is expected to start in Fall 2023, though specific dates are negotiable.

Interested candidates should submit a CV, short (1-2 pages) cover letter describing your research interests and fit within the lab, and contact information for 2-3 references. Informal inquiries are also welcomed and should be addressed to Dr. Nicholas Mancuso at nmancuso@usc.edu.

Prepare students to be able to develop novel, effective, and efficient computational methods to address the challenges arisen from emerging data. Train our students to have a solid foundation of statistics, algorithms, and biology with a rigorous curriculum, and build their ability to analyze data and formulate problems by research projects. Minimum Education: Ph.D. or equivalent doctorate within previous five years Minimum Experience: 0-1 year Minimum Field of Expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures and analysis methods. Preferred Field of Expertise: Publications in peer-reviewed journals in the same or related field