Research Assistant (Pharmacy And Pharmaceutical Sciences)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal. NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Pharmacy-and-Pharmaceutical-Sciences%29/33600-en_GB/?st=30C3D10945F579E39FF80B1D38D6C9F0E2BF1027 We regret that only shortlisted candidates will be notified. Job Description Prof. Eric Chan’s laboratory in the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore, invites applications for a Research Assistant position to support a computational research project titled “Hybrid Physiological Intelligence for Metabolic Ageing”. The project aims to develop a mechanism-based PBPK–QSP–AI/ML-driven framework to support healthy longevity by identifying the right intervention, care strategy, drug, dose, and timing across ageing-related physiological and clinical trajectories. The work will involve clinical data curation, review of ageing-related lifestyle, pharmacological, and gerotherapeutic intervention evidence, development and calibration of PBPK–QSP models, and translation of model outputs into interpretable decision-support workflows for healthy longevity applications. Key Responsibilities 1. Identify, curate, and organize relevant Singapore-based clinical datasets, published clinical studies, and intervention trial evidence related to healthy longevity, ageing, cardiometabolic risk, pharmacological interventions, and lifestyle interventions. 2. Perform data cleaning, variable mapping, exploratory analysis, and preparation of datasets for computational modelling. 3. Support literature review and extraction of model-relevant information from published studies on lifestyle and therapeutic intervention related to ageing. 4. Assist in the development, implementation, calibration, and validation of PPBPK, pharmacodynamic, and QSP model components for ageing-related physiological systems and pharmacological interventions. 5. Conduct computational simulations to evaluate intervention-response relationships, pharmacotherapy optimization scenarios, drug–drug interaction risks, adherence-related scenarios, and ageing-related physiological changes. 6. Support AI/ML-enabled analysis, visualization, and development of interpretable decision-support workflows for healthy longevity applications. 7. Prepare technical documentation, figures, reports, presentations, and manuscript drafts arising from the project. 8. Coordinate with project investigators, collaborators, data teams, and administrative staff to support timely project execution and reporting. Qualifications / Discipline: • A bachelor's or master's degree in pharmacy, pharmacology, biomedical sciences, bioinformatics, computational biology, biomedical engineering, applied mathematics, statistics, computer science, or related discipline. Skills: • Good quantitative, analytical, and problem-solving skills. • Experience with at least one programming or statistical analysis language, such as R, Python, MATLAB, Julia, or similar. • Interest in computational modelling, systems pharmacology, clinical pharmacology, AI/ML, digital health, or healthy longevity research. • Ability to work independently and collaboratively in a multidisciplinary research environment. • Good organizational, writing, and communication skills. Experience: Prior experience in one or more of the following areas would be advantageous: • PBPK, pharmacokinetic/pharmacodynamic, QSP, systems biology, or mathematical modelling. • Clinical data analysis, electronic health record data, or intervention trial datasets. • Machine learning, statistical modelling, or data visualization. • Pharmacology, ageing biology, cardiometabolic disease, drug–drug interactions, or medication optimization. • Scientific writing, literature review, and manuscript preparation. Candidates without experience are welcome to apply.