Postdoctoral Scholar in Environmental and Molecular Epidemiology
4 weeks ago
The Goodrich Lab in the Department of Population and Public Health Sciences at the Keck School of Medicine seeks a passionate and committed Postdoctoral Scholar Research Associate to join a dynamic, interdisciplinary team engaged in translational environmental health research. Our NIH-funded projects span environmental and molecular epidemiology, computational toxicology employing artificial intelligence, experimental studies (in vitro and in vivo), and advanced multiomics data science. We are looking for candidates with expertise or interest in children's environmental health, biostatistics, and data science, especially as applied to environmental chemical exposures, type 2 diabetes, kidney disease, and cancer. The ideal candidate will be open-minded, eager to learn, and enthusiastic about engaging in our multidisciplinary research efforts.
Key responsibilities for this role include:
- Initiating, planning, and overseeing research projects
- Leading the analysis of complex multiomics data, including single-cell and spatial omics, using R or Python; experience with these datasets is helpful but not required
- Preparing statistical reports and presentations
- Presenting findings at regional or national scientific meetings
- Mentoring or overseeing biostatisticians and graduate students
- Contributing to the writing of scientific manuscripts and grant proposals
- Collaborating with team members on cross-disciplinary research projects.
- Keeping abreast of the latest research methodologies and participating in relevant training sessions and conferences
Minimum qualifications required for this position include:
A doctoral degree (Ph.D., Sc.D.) in Environmental Health, Epidemiology, Biostatistics, or closely related fields. Demonstrated ability to manage multiple projects simultaneously. Strong scientific writing and oral communication skills. Strong analytical and problem-solving skills. Intermediate to advanced coding skills in R or Python. The ability to collaborate effectively with a diverse team.
Preferred qualifications include:
- Experience in analyzing large biological datasets (e.g., scRNAseq, metabolomics, proteomics)
- Proficiency in collaborative coding environments (e.g., git/GitHub)
This role presents significant opportunities for professional development and growth. Given the interdisciplinary nature of our work, we encourage applications from candidates with a broad range of academic backgrounds.
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