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.

Position Overview:
We are seeking multiple highly motivated Postdoctoral Research Fellow with expertise in systems pharmacology, machine learning, and/or data science to join our interdisciplinary team. The successful candidate will work at the intersection of drug development, regulatory science, and advanced computational modeling. This position will involve integrating mechanistic modeling and machine learning methods to analyze and predict drug properties, patient responses, and benefit-risk profiles, using real-world and regulatory datasets. This position will have a great opportunity to interact with top experts in the field to directly address drug development issues.

- 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.