Computational Chemistry Co-op , Closed-Loop Ligand Design and Optimization; Doctoral) Haven, Connecticut, United

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.

• 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 approaches through regular updates and a final presentation.

• Currently enrolled in a Ph.D. program in synthetic organic chemistry or a related field with a focus on computational chemistry.
• Practical experience with quantum chemistry-based calculations (such as DFT calculations), including transition-state theory and energy calculation workflows.
• Hands-on laboratory experience in synthetic organic chemistry, catalysis, or a related area, including the ability to execute experiments safely and interpret experimental results.
• Understanding of organic stereo chemistry and asymmetric catalysis concepts relevant to chiral ligand design.
• Understanding of Python (or other programming languages), including use of relevant scientific libraries.
• Foundational understanding of machine learning concepts: model training, validation, overfitting, and regression/classification frameworks.
• Strong written and verbal communication skills.
• Demonstrated ability to work collaboratively in a team environment and contribute to shared goals.
• A strong work ethic and high level of self-motivation with a strong desire to learn and contribute to shared project goals.
• Must be available during the full 6-month term and maintain general availability during standard business hours.
• US Work Authorization is required at time of application.
• This role does not provide OPT sponsorship or support. Candidates authorized to work under CPT may be considered, subject to verification of eligibility and applicable company requirements.

• Prior experience in cheminformatics or data-driven research projects.
• Prior experience with Bayesian optimization frameworks or active learning pipelines.
• Familiarity with molecular descriptor generation, molecular…