Computational Chemistry for Chiral Catalysis Co-op

Alexion Pharma Spain S.L.; New Haven (CT); Presencial; USD 55 000 - 77 000; Tempo integral

Resumo da oferta

Alexion Pharma SPD is seeking a motivated PhD student for a 6-month co-op in 1H 2027 focused on developing a closed-loop platform that combines computation and experiment to accelerate chiral ligand design. The role emphasizes DFT, Bayesian optimization, and ML to guide experimental optimization, with hands-on lab work and regular updates.

Ideal candidates are pursuing a PhD in synthetic organic chemistry or related computational field, have Python/Machine Learning familiarity, and can work in a

Qualificações

• Currently enrolled in a Ph.D. program focusing on computational chemistry.
• Hands-on experience with quantum chemistry calculations (DFT) and transition-state theory.
• Laboratory experience in synthetic organic chemistry or catalysis.
• Understanding of organic stereochemistry and asymmetric catalysis.
• Proficiency in Python and ML concepts; strong communication and teamwork.

Responsabilidades

• Partner with chemists to identify selectivity challenges and validate predictions against experiments.
• Perform DFT transition-state calculations for a seed library of chiral ligands.
• Develop ML surrogate models predicting diastereomeric ratios from descriptors; run Bayesian optimization.
• Test computational predictions experimentally and refine subsequent design cycles.
• Maintain and document the closed-loop pipeline (DFT ← ML ← BO ← Exp) for reproducibility.
• Communicate challenges and results through regular updates and a final presentation.

Conhecimentos

Python programming

Machine learning basics

Data-driven research

Team collaboration

Formação académica

PhD in synthetic organic chemistry or related field focusing on computational chemistry

Ferramentas

DFT calculations

Bayesian optimization

NumPy/SciPy

Descrição da oferta de emprego

Alexion Pharma SPD is seeking a motivated PhD student for a 6-month co-op in 1H 2027 focused on developing a closed-loop platform that combines computation and experiment to accelerate chiral ligand design. The role emphasizes DFT, Bayesian optimization, and ML to guide experimental optimization, with hands-on lab work and regular updates.

Ideal candidates are pursuing a PhD in synthetic organic chemistry or related computational field, have Python/Machine Learning familiarity, and can work in a