Postdoctoral Researcher – AI for Retrosynthesis & Reaction Prediction

About the role

We are looking for a highly motivated postdoctoral researcher to join an interdisciplinary team focused on artificial intelligence for retrosynthetic pathway planning and reaction outcome prediction. The role combines cutting‑edge machine learning with practical synthetic chemistry to enable greener and more efficient routes for complex molecules.

Key responsibilities

• Develop and implement AI/ML models (e.g., graph neural networks, transformer‑based architectures) for retrosynthetic pathway prediction.
• Apply deep‑learning techniques to forecast reaction outcomes, optimise conditions and discover novel synthetic routes.
• Curate and manage reaction datasets from literature, patents and proprietary sources for model training and validation.
• Integrate retrosynthesis tools with cheminformatics platforms and molecular‑modelling software.
• Collaborate with synthetic chemists to experimentally validate predicted routes and improve laboratory workflows.
• Publish research in high‑impact journals and present findings at conferences and workshops.
• Mentor graduate students and contribute to capacity‑building activities within the team.

Required profile

• Ph.D. in Chemistry, Computational Chemistry, Cheminformatics, Computer Science or a related discipline.
• Demonstrated expertise in machine‑learning or deep‑learning applied to chemical reaction prediction or retrosynthesis.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch or JAX.
• Hands‑on experience with cheminformatics tools (e.g., RDKit, Open Babel) and reaction databases (e.g., Reaxys, USPTO, Pistachio).
• Solid understanding of organic synthesis and reaction mechanisms.
• Strong publication record in AI and computational chemistry.

Required skills

• Python programming
• TensorFlow
• PyTorch
• JAX
• RDKit
• Open Babel
• Graph neural networks
• Transformer architectures
• Reaction databases (Reaxys, USPTO, Pistachio)