Co-op: Computational Chemistry for Chiral Ligand Design

Alexion Pharmaceuticals, Inc.; New Haven (CT); On-site; USD 80,000 - 120,000; Full time

Job summary

Alexion Pharmaceuticals, Inc. seeks a motivated PhD candidate for a 6-month co-op in 1H 2027 to advance data-driven catalyst design within the SPD team.

The role builds a closed-loop platform combining DFT, ML, and Bayesian optimization to accelerate chiral ligand development, followed by experimental validation and knowledge transfer across the team. This hands-on project spans translational research and early development.

Qualifications

• Currently enrolled in a Ph.D. program in synthetic organic chemistry or a related field with a computational focus.
• Practical experience with quantum chemistry-based calculations (DFT) including transition-state theory and energy calculation workflows.
• Hands-on laboratory experience in synthetic organic chemistry, catalysis, or a related area, with ability to execute experiments safely and interpret results.
• Understanding of organic stereochemistry and asymmetric catalysis concepts relevant to chiral ligand design.
• Understanding of Python (or other programming languages) and relevant scientific libraries.
• Foundational understanding of machine learning concepts: model training, validation, overfitting, and regression/classification frameworks.
• Strong written and verbal communication skills and ability to work collaboratively.

Responsibilities

• Partner with synthetic chemists to identify key selectivity challenges, co-design training sets, and validate predictions against experiments.
• Perform DFT transition-state calculations to determine energies/geometries for seed chiral ligands.
• Develop ML surrogate models that predict diastereomeric ratios from descriptors and implement Bayesian optimization campaigns.
• Test computational predictions experimentally and refine subsequent design cycles.
• Build, maintain, and document the closed-loop pipeline (DFT -> ML -> BO -> Exp) for reproducibility and transfer.
• Communicate scientific challenges and approaches through regular updates and a final presentation.

Skills

Python

DFT calculations

Machine learning

Bayesian optimization

Cheminformatics

Data-driven research

Team collaboration

Communication skills

Education

Ph.D. in synthetic organic chemistry (or related field)

Tools

DFT software

Bayesian optimization frameworks

Active learning pipelines

Job description

Alexion Pharmaceuticals, Inc. seeks a motivated PhD candidate for a 6-month co-op in 1H 2027 to advance data-driven catalyst design within the SPD team.

The role builds a closed-loop platform combining DFT, ML, and Bayesian optimization to accelerate chiral ligand development, followed by experimental validation and knowledge transfer across the team. This hands-on project spans translational research and early development.