Postdoc Fellow: Systems Pharmacology & ML in Drug Development

University of North Carolina - Chapel Hill; Elkins Hills (NC); On-site; USD 60,000 - 80,000; Full time

Job summary

The University of North Carolina – Chapel Hill invites applications for a Postdoc Research Fellow in the Dr. Cao Lab in Chapel Hill, NC.

The role focuses on integrating mechanistic modeling with machine learning to analyze drug properties and patient responses using real-world and regulatory datasets. Candidates should have a PhD in a relevant field, a strong publication record, and experience with computational modeling and PK/PD.

Qualifications

• PhD in Biostatistics, Bioinformatics, Data Science, Pharmaceutical Sciences, CS, Regulatory Science, or related fields.
• Strong publication record and expertise in computational modeling, data analysis, and ML methods.
• Excellent scientific writing and communication skills; PK/PD modeling or systems pharmacology is desirable.

Responsibilities

• Develop datasets and modeling approaches to support drug development and regulatory evaluation.
• Relate PK features to patient characteristics and predict relationships using ML with trial data.
• Collaborate across disciplines to integrate mechanistic modeling with machine learning.
• Work with regulatory science datasets to inform decision-making.

Skills

Computational modeling

Data analysis

Statistical / ML methods

Scientific writing

Communication skills

Regulatory science familiarity

Education

PhD in Biostatistics, Bioinformatics, Data Science, Pharmaceutical Sciences, Computer Science, Regulatory Science, or related fields

Tools

PK/PD modeling

Regulatory datasets analysis

Job description

The University of North Carolina – Chapel Hill invites applications for a Postdoc Research Fellow in the Dr. Cao Lab in Chapel Hill, NC.

The role focuses on integrating mechanistic modeling with machine learning to analyze drug properties and patient responses using real-world and regulatory datasets. Candidates should have a PhD in a relevant field, a strong publication record, and experience with computational modeling and PK/PD.