Computational Chemistry Co-op , Closed-Loop Ligand Design and Optimization (Doctoral)

Alexion Pharmaceuticals, Inc.; New Haven (CT); Presencial; USD 80.000 - 120.000; Jornada completa

Descripción de la vacante

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.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python

DFT calculations

Machine learning

Bayesian optimization

Cheminformatics

Data-driven research

Team collaboration

Communication skills

Educación

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

Herramientas

DFT software

Bayesian optimization frameworks

Active learning pipelines

Descripción del empleo

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.

You will be responsible for:

• 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.

You will need to have:

• 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 stereochemistry 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.

We would prefer for you to have:

• 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 fingerprints, or structure-activity/selectivity relationship modeling.


• Experience with feature-attribution or model-interpretability methods applied to chemical or materials data.

Physical and Mental Requirements:

• The duties of this role are generally conducted in an office environment. As is typical of an office-based role, you must be able, with or without an accommodation to: use a computer; engage in communications via phone, video, and electronic messaging; engage in problem solving and non-linear thought, analysis, and dialogue; collaborate with others; maintain general availability during standard business hours.

Compensation for this role is $48 per hour.

Date Posted: 03-Sep-2026

Closing Date: 05-Nov-2026

Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.