Postdoc Research Fellow - Dr. Cao Lab

About the role

About the Group: Our research group is dedicated to developing innovative datasets and modeling approaches to support drug development and regulatory evaluation. We focus on two major research areas: 1. Drug Pharmacokinetics (PK) and Clinical Relevance – Investigating how pharmacokinetic features relate to patient characteristics, drug efficacy, and safety profiles, and how these relationships can be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory datasets to build models and generate evidence that can inform regulatory decision-making and accelerate the development of safe and effective therapeutics.

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, patient responses, and benefit-risk profiles, using real-world and regulatory datasets.
• Interact with top experts in the field to directly address drug development issues.

Requirements

• 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.
• Excellent communication and scientific writing skills.
• 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.

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.
• Excellent communication and scientific writing skills.
• 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.

Skills

• Expertise in computational modeling, data analysis, and statistical/machine learning methods.
• Strong publication record in relevant disciplines.
• Excellent communication and scientific writing skills.
• Experience in mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling or systems pharmacology.
• Familiarity with regulatory science or clinical trial data.

Benefits

Dependent on experience and qualifications.

Pay

Dependent on experience and qualifications.

Schedule

40 hours per week.