About the role#
The Synthetic Product Development team is seeking a Ph.D. student for a 6-month co-op. You will focus on building a closed-loop platform that combines computational tools with experimental testing to improve chiral ligand development. This role involves applying computational chemistry and machine learning to real-world challenges in catalyst design and ligand discovery.
What you'll do#
- Partner with synthetic chemists to identify selectivity challenges and validate computational predictions against experimental results.
- Perform DFT transition-state calculations to determine energies and geometries for chiral ligands.
- Develop machine learning surrogate models and implement Bayesian optimization to identify optimal ligands.
- Test computational predictions in the lab to refine design cycles.
- Build and document the closed-loop computational pipeline to ensure reproducibility and scalability.
What you'll need#
- Current enrollment in a Ph.D. program in synthetic organic chemistry or a related field with a focus on computational chemistry.
- Practical experience with quantum chemistry calculations, specifically DFT.
- Hands-on laboratory experience in synthetic organic chemistry or catalysis.
- Proficiency in Python and relevant scientific libraries.
- Foundational knowledge of machine learning, including model training and validation.
- Ability to work on-site during the full 6-month term.
- US work authorization is required. This role does not offer sponsorship.
Location & details#
- Location: New Haven, Connecticut.
- Term: Winter 2027 or Summer 2027.
- Compensation: $48 per hour.
- Status: Full-time, on-site.
About AstraZeneca
AstraZeneca is a public pharmaceutical manufacturing company. It focuses on research, development, and global business operations. The company maintains offices in Cambridge and Sodertalje. It employs over 80,000 people.
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