Postdoc: Systems Pharmacology & ML Research Fellow

UNC-Chapel Hill; Chapel Hill (NC); On-site; USD 60,000 - 85,000; Full time

Benefits offered by this job

Medical coverage

Vision coverage

Paid leave

Professional development

Job summary

University of North Carolina at Chapel Hill seeks multiple Postdoctoral Research Fellows in the Dr. Cao Lab to advance systems pharmacology, machine learning, and data science for drug development and regulatory science.

The position emphasizes integrating mechanistic modeling with real-world data and regulatory datasets, with opportunities for collaboration across disciplines. The role targets individuals with PhDs in relevant fields, a strong publication record, and experience in computational

Qualifications

• PhD in Biostatistics, Bioinformatics, Data Science, Pharmaceutical Sciences, Computer Science, Regulatory Science, or related fields.
• Strong publication record in relevant disciplines.
• Demonstrated expertise in computational modeling, data analysis, and statistical/machine learning methods.
• Experience in mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling or systems pharmacology is highly desirable.
• Familiarity with regulatory science or clinical trial data is a plus.

Responsibilities

• Work at the intersection of drug development, regulatory science, and advanced computational modeling.
• Integrate mechanistic modeling and machine learning methods to analyze and predict drug properties and patient responses.
• Collaborate with the lab and regulatory teams to generate evidence for decision-making and publication.
• Prepare and present research findings and contribute to grant and manuscript writing.

Skills

Publication record

Computational modeling

Data analysis

Statistical ML

PK/PD modeling

Regulatory science

Education

PhD in Biostatistics/Bioinformatics/Data Science/Pharmaceutical Sciences/CS/Regulatory Science

Job description

University of North Carolina at Chapel Hill seeks multiple Postdoctoral Research Fellows in the Dr. Cao Lab to advance systems pharmacology, machine learning, and data science for drug development and regulatory science.

The position emphasizes integrating mechanistic modeling with real-world data and regulatory datasets, with opportunities for collaboration across disciplines. The role targets individuals with PhDs in relevant fields, a strong publication record, and experience in computational