Post Doctoral Fellow in Transcriptomics, P&G Digital Accelerator @ UC

Job Overview

The P&G Digital Accelerator@UC is seeking a motivated PhD toxicologist or pharmacologist with expertise in transcriptomics for a 24-month post-doctoral position partnering with faculty and staff from the University of Cincinnati and members of the P&G Human Safety team. In this role the candidate will help drive the independent design of cell-based transcriptomics experiments to meet project needs, integrating their work with existing in vitro and in vivo data to support consumer chemical safety assessments across multiple P&G businesses.

Essential Functions

• This research will use cutting-edge techniques leveraging automation and high-throughput methods to generate high-quality and large datasets providing novel insights into improving predictability of in vitro assays for consumer safety of P&G chemicals. The postdoc would have access to and leverage existing transcriptomics data generated for similar chemicals across multiple cell types.
• Experimental design, provide guidance in experimental execution, data analysis, and statistical quality control.

Required Education

PhD in Toxicology, Pharmacology, Pathology, or related life science fields

Required Experience

• Capable of independently designing and overseeing the execution of experiments, data interpretation, and identifying appropriate follow-up strategies. Comfort with production, analysis, and interpretation of large datasets.
• Strong experimental design and troubleshooting.
• Data analysis and quality control.

Additional Qualifications Considered

• Excellent project management and teamwork skills. Ability to multitask and work within timelines.
• Ability to resolve key project hurdles and assumptions by effectively utilizing available information and technical expertise.
• Demonstrated scientific writing skills and strong communication skills.
• Global mindset to thrive in a diverse culture and environment.
• Familiarity with interpretation of large transcriptomic datasets.
• Experience with high-throughput or automated cell-based assays.
• Familiarity with computational data analysis methods.

Physical Requirements/Work Environment

• Office environment/no specific unusual physical or environmental demands.