Computational Chemistry Co-op , Closed-Loop Ligand Design and Optimization (Doctoral)

This is what you will do

Alexion's Synthetic Product Development (SPD) team is seeking a highly motivated graduate student currently enrolled in a Ph.D. program in synthetic organic chemistry or a related field, with an interest in data-driven catalyst design, for a 6-month co-op program in 1H 2027.

This co-op will focus on developing a closed-loop computational and experimental platform that integrates state-of-the-art computational tools and experimental testing to accelerate chiral ligand development and optimization. SPD is a multidisciplinary team of scientists supporting synthetic drug substance, drug product, and analytical development across Alexion’s synthetic portfolio, spanning preclinical development through commercial launch. This position offers a unique opportunity to gain hands-on experience in applying computational chemistry and AI technologies to real-world challenges in diastereoselective catalyst design, ligand discovery, and computationally guided experimental optimization.

You will be responsible for

• Partnering with synthetic chemists to identify key selectivity challenges, co-design chemically reasonable training sets, and validate computational predictions against experimental outcomes.
• Performing DFT transition-state calculations to determine energies and geometries for a seed library of chiral ligands.
• Developing machine learning surrogate models that predict diastereomeric ratios from molecular descriptors and implementing Bayesian optimization campaigns to sequentially identify optimal ligand candidates balancing selectivity, reactivity, and synthetic feasibility.
• Testing computational predictions experimentally and using the resulting data to refine subsequent design cycles.
• Building, maintaining, and documenting the closed-loop computational pipeline (DFT → ML → BO → Exp) to ensure reproducibility, scalability, and knowledge transfer to the broader SPD team.
• Communicating scientific challenges and the technical approac