2027 Future Talent Program - Translational Sciences and Outsourcing - Intern

Merck seeks a PhD student intern for a 12-week summer program to benchmark machine learning methods for predicting pharmacokinetic profiles. The role involves analyzing large pharmaceutical datasets, implementing and comparing ML models, designing rigorous evaluation strategies, and contributing to a publishable benchmark study within the PDMB department.

Responsibilities

• Prepare and analyze large-scale pharmacokinetic and molecular datasets for reproducible modeling
• Implement and benchmark classical ML, molecular-structure-based, and time-series models for PK curve prediction
• Design and compare multiple evaluation strategies to assess model generalization and identify data leakage
• Collaborate with scientists to interpret results and ensure scientific validity of findings
• Document methods and results; contribute to draft manuscript and end-of-summer presentation

Requirements

• PhD student in computational chemistry, cheminformatics, machine learning, computer science, or related field
• Strong publication record in top-tier peer-reviewed journals or ML conferences (ICML, ICLR, NeurIPS)
• Advanced Python programming with PyTorch or similar ML/DL frameworks
• Experience building reproducible analysis pipelines and understanding data leakage in model evaluation
• Strong technical communication skills with ability to design and interpret comparative studies
• Demonstrated interest in computational drug discovery benchmarking (preferred: pharmacokinetics, ADMET, or molecular representations)

Compensation: $39,108–$111,111