The University of Southern California (USC), founded in 1880, is located in the heart of downtown Los Angeles and is the largest private employer in the City of Los Angeles. USC is consistently ranked among the nationâs most prestigious universities, and the USC Leonard Davis School of Gerontology features one of the worldâs best degree and research programs in gerontology. TheÂ USC Davis SchoolÂ has an international reputation as a hub of aging research and, with additional strong programs throughout the university, leads the way in defining and advancing the field of gerontology.
The USC Leonard Davis School of Gerontology is seeking a Biostatistician I to join its team! This person will be working with genomic data from model organisms (i.e. mouse, killifish, yeast, worms, human cells), and conduct analyses under the supervision and with the guidance of the PIs. They will make original, scientific contributions to projects in the laboratory. This includes analyzing pathways controlling aging using computational biology approaches. This is an opportunity for a reliable and self-motivated person to participate in cutting-edge aging research. Primary responsibilities are to conduct genomic data analyses and to assist the labs with their design. Other duties include assisting lab members with basic data analysis and helping to train them in new or established lab analytical pipelines.
This is a shared position between the Benayoun and Vermulst laboratories at the USC Leonard Davis School of Gerontology, which both use genomic data analysis to understand new features of aging regulation.Specifically, the Benayoun lab focuses on the study of how aging influences gene regulation, and in return, how modulation of the epigenome and transcriptome can influence the aging process. To study this question, we use a unique combination of high-throughput 'omics' approaches, machine-learning, and experimental validation in primary cell cultures and vertebrate aging models (e.g. mouse, African turquoise killifish, etc.). The goal of the lab is to discover novel target genes and pathways playing a fundamental role in healthy and pathological aging while leveraging the power of big data to directing experimental interventions.
The Vermulst laboratory focuses primarily on the effect of biological errors in aging and disease. Many age-related diseases, including Alzheimerâs, are caused by proteotoxic stress. We have found a link between transcription errors and proteotoxic stress that helps explain the etiology of these diseases and are now identifying the molecular mechanisms that give rise to these transcription errors. In addition, we are actively generating new models in worms and mice to expose the role of mitochondrial mutagenesis and proteomics in aging organisms, to determine how these processes contribute to age-related pathology.
These two collaborating laboratories are seeking a skilled and motivated scientist to join their teams as a Biostatistician I and help make leading contributions to aging research. This is a unique opportunity for a computational scientist to work closely with experimental biologists, analyze cutting-edge data (e.g. ATAC-seq, RNA-seq, single-cell data, circle-sequencing, etc.), and help develop new frameworks for data integration.
Job responsibilities will include but are not limited to:
Performing data analysis for a major portion of the research project. Maintain detailed records of analyses and outcomes.
Writing the programming code to accomplish the data analysis.
Interpret and analyze results, often where few guidelines exist. Based on prior results, contribute to the development of new pipelines and procedures or adaptation of existing ones.
Establish new pipelines and/or use basic bioinformatics pipelines to process and analyze laboratory datasets
Perform ongoing literature review to remain current with new procedures and related research; present findings for the lab work-group.
Contribute substantively to the preparation of papers for publication, especially in the results section. May contribute to the publication of findings and present ongoing work and findings to colleagues at academic conferences.
Supervise (either formally or informally) staff or students as needed, including oversight and instruction on techniques, as well as consultation on project work. Help train other lab members by providing input on computational biology analysis.
The ideal candidate will hold a Masterâs degree in biostatistics, computer science, computational biology or a related field. They will some experience with basic data analysis techniques, working knowledge of statistical software (e.g. R), and scripting software (e.g. perl/python). This position requires strong organizational skills, and the ability to interact with the PIs and other lab members on a daily basis.
This position is funded by a research grant. Accordingly, this position is contingent upon receipt of the necessary funding from this or any subsequent grant to continue employment.Â â
Benefits include a competitive salary, a rich array of USC benefits including healthcare (including dental and vision), 403(b), tuition reimbursement, national holidays, paid vacation, sick time and more.
USC is an equal opportunity, affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other characteristic protected by law or USC policy. USC will consider for employment all qualified applicants with criminal histories in a manner consistent with the requirements of theÂ Los Angeles Fair Chance Initiative for HiringÂ ordinance.
If you are interested, please submit a fully completed application. Include a cover letter and resume with references. Thank you for your interest!
Minimum Education: Master's degree, Combined experience/education as substitute for minimum education Minimum Experience: 1 year, Combined education/experience as a substitute for the minimum experience Minimum Field of Expertise: Experience in Biostatistics, Statistics or related field. Experience with SAS, R or STATA.
Internal Number: REQ20084422
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