Closed-Loop Catalyst Design Co-op

AstraZeneca plc; New Haven (CT); On-site; USD 55,000 - 83,000; Full time

Job summary

AstraZeneca plc's Alexion unit in the US seeks a motivated PhD candidate for a 6-month co-op in 1H 2027 to advance data-driven catalyst design. The role blends computational chemistry, machine learning, and experimental validation to accelerate chiral ligand development across the SPD portfolio.

You'll collaborate with synthetic chemists to identify selectivity challenges, run DFT calculations, build ML models, and drive Bayesian optimization campaigns, while documenting the closed-loop workflow

Qualifications

• PhD candidate focusing on computational chemistry and synthesis.
• Experience with quantum chemistry calculations and transition states.
• Lab experience in synthetic organic chemistry and catalysis.
• Familiarity with Python and ML concepts, strong communication.

Responsibilities

• Co-design training sets and validate predictions against experiments.
• Perform DFT transition-state calculations for seed ligands.
• Develop ML surrogate models and Bayesian optimization campaigns.
• Test predictions experimentally and refine design cycles.
• Maintain closed-loop pipeline (DFT -> ML -> BO -> Exp).
• Communicate challenges and results with the SPD team.

Skills

Python

DFT calculations

Machine learning

Synthetic chemistry

Team collaboration

Education

PhD candidate in synthetic organic chemistry

Tools

Bayesian optimization

Molecular descriptors

DFT software

Job description

AstraZeneca plc's Alexion unit in the US seeks a motivated PhD candidate for a 6-month co-op in 1H 2027 to advance data-driven catalyst design. The role blends computational chemistry, machine learning, and experimental validation to accelerate chiral ligand development across the SPD portfolio.

You'll collaborate with synthetic chemists to identify selectivity challenges, run DFT calculations, build ML models, and drive Bayesian optimization campaigns, while documenting the closed-loop workflow