A postdoctoral research position is available in the lab of Dr. Adam de Smith in the Center for Genetic Epidemiology, Department of Preventive Medicine at the University of Southern California, Keck School of Medicine. The de Smith lab uses cutting-edge epidemiological tools to investigate the causes of childhood leukemia, the most common cancer in children. In particular, we have a strong interest in using genomic data to understand the increased risk of acute lymphoblastic leukemia (ALL) in individuals of self-reported Hispanic/Latino ethnicity, and the increased risk of acute leukemia in children with Down syndrome.

The successful candidate will have opportunities to analyze large-scale germline genotyping data, as well as tumor whole-genome sequencing data from childhood leukemia patients to investigate the etiologic role of early life environmental exposures and the prenatal origins of childhood leukemia. Additional information about our lab and research can be found at: https://sites.usc.edu/childhoodcancer/.

The Center for Genetic Epidemiology (https://keck.usc.edu/genetic-epidemiology-center/) is closely linked with the Departments of Preventive Medicine, Translational Genomics, and Quantitative Computational Biology. The Department of Preventive Medicine is one of the nation's leading research programs in epidemiology and biostatistics, with particular expertise in genetics research in diverse populations, and offers exceptional resources and collaborative opportunities for postdoctoral fellows. In particular, we work closely with research groups focusing on genetic epidemiology, statistical and functional genomics, and population genetics. 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 genetics, genetic epidemiology, biology, or a related field. Proficiency in one or more programming languages (e.g. R, python, perl, C++, etc.) is essential. Some laboratory experience is preferred. Competitive applicants will also have extensive experience in conducting human genetics research and analyzing large genetic datasets. Preference will be given to candidates with a strong publication record, evidence of substantial research productivity, and an ability to successfully communicate scientific information. Review of applications will begin immediately and will continue until the position is filled. The position start date is negotiable.

Interested candidates should apply in Workday, https://wd5.myworkday.com/usc/login.flexPlease submit a CV, a short (1-2 pages) cover letter describing your research interests andfit within the lab, and contact information for 2-3 references. Informal inquiries are also welcomed and should be addressed to Dr. Adam de Smith at desmith@usc.edu

The annual base salary range for this position is $68,000 - $72,000. 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.

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