Postdoctoral Position in Automated Chemistry and Machine Learning

About the role

Two-year postdoctoral position at the University of Graz on automated chemistry, combining laboratory robotics, high-throughput synthesis, and machine learning for closed-loop materials and drug discovery. The host group runs one of the few self-driving laboratory setups in central Europe. Deadline 22 May 2026.

Responsibilities

• Design and operate closed-loop experiments where ML models propose syntheses, robots execute, and results feed back into the model.
• Build ML pipelines for reaction yield prediction, retrosynthesis, and Bayesian experimental design.
• Publish in Nature Chemistry, Chem, JACS, and ML venues such as NeurIPS or ICML.
• Mentor PhD students and contribute to grant writing.

Requirements

• PhD in computer science, chemistry, chemical engineering, or a closely related field, with demonstrated work at the intersection.
• Strong machine learning skills, particularly Bayesian optimisation, generative models, or graph neural networks for molecules.
• Python proficiency; familiarity with RDKit, BoTorch, or similar.
• Fluent English. German is welcomed but not required.

Nice to have

• Hands-on lab automation or chemistry experience.
• First-author publications at the intersection of ML and chemistry.
• Familiarity with Bayesian optimisation libraries and active learning loops.

How to apply

Open the University of Graz jobs portal at the apply link and search “Automated Chemistry Postdoc”. Submit CV, publication list, research statement, Master’s and PhD certificates, and two reference letters before 22 May 2026.