Postdoctoral Position in AI-driven Retrosynthesis and Reaction Prediction

About the role

We are seeking a highly motivated postdoctoral researcher to join an interdisciplinary team focused on artificial intelligence‑driven retrosynthesis and reaction prediction. The role combines cutting‑edge machine learning with computational chemistry to create sustainable synthetic routes for complex molecules in pharmaceuticals, agrochemicals and related sectors.

Key responsibilities

• Design, develop and benchmark AI/ML models (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 large 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 refine laboratory workflows.
• Publish research findings in high‑impact journals and present at conferences.
• Mentor graduate students and contribute to team capacity‑building activities.

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.
• Strong publication record in AI and computational chemistry.
• Excellent problem‑solving abilities and capacity to work in a multidisciplinary environment.

Required skills

• Python programming.
• ML frameworks such as TensorFlow, PyTorch or JAX.
• Cheminformatics tools (RDKit, Open Babel) and reaction databases (Reaxys, USPTO, Pistachio).
• Graph neural networks and transformer‑based model development.
• Experience with cloud computing and high‑performance computing resources.