2027 Future Talent Program – Translational Sciences and Outsourcing – Intern
Core
Intern developing and benchmarking AI/ML methods for pharmacokinetic curve prediction to support drug discovery decision-making.
Role type
Research intern (PhD level)
Builds
Reproducible benchmark studies and draft manuscripts for peer-reviewed publication
Domain
Pharmaceutical research, computational drug discovery, pharmacokinetics
Deliverable
research
Required skills
Python, PyTorch, machine learning, deep learning, reproducible research pipelines, data leakage analysis, scientific writing
Preferred skills
pharmacokinetics, pharmacometrics, cheminformatics, molecular representations, graph neural networks, time-series modeling, physics-informed machine learning
Technologies
PyTorch, RDKit, Chemprop, Uni-Mol
Responsibilities
Analyze large-scale pharmacokinetic and molecular datasets; Implement and benchmark classical ML and time-series models; Design evaluation settings to quantify performance across compound novelty; Collaborate with scientists to interpret results; Document methods and results for a draft manuscript; Present project outcomes at an intern symposium
Seniority
PhD student, hands-on research
