Postdoctoral Scientist (Machine Learning)

Join Quantemol to advance the frontiers of plasma science through the application of machine learning. You will design and implement algorithms to predict physical and chemical properties, and develop methods to expedite simulation times in plasma modelling. Working closely with scientists and software developers, your work will drive innovative computational tools and high-impact research. This is a full-time position based in London (hybrid working model with around 2-4 office days per month), with a salary range of £35,000 to £45,000., * Design and implement machine learning models for property prediction and plasma simulation.

• Develop algorithms to accelerate complex plasma modelling workflows.
• Collaborate with scientists to integrate ML-driven predictions into Quantemol's software platforms.
• Present findings at international conferences, workshops and to clients; publish research outcomes in peer-reviewed journals.
• Contribute to project planning, reporting, and achievement of research milestones.
• Participate in consultancy projects involving machine learning applications to plasma science.
• Work closely with software developers to deploy robust and efficient ML pipelines.
• Contribute to a collaborative, interdisciplinary research environment.

Required

• A PhD in Chemistry, Physics, Computer Science, Applied Mathematics, or a closely related discipline.
• Proven research experience in machine learning applied to scientific domains.
• Strong record of publications in peer-reviewed journals or impactful technical reports.
• Proficiency in Python and experience with ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn).
• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.

Preferred

• Experience applying machine learning to computational physics, chemistry, or materials science.
• Familiarity with plasma modelling or electron-molecule interaction data.
• Experience with high-performance computing environments.
• Knowledge of version control systems (e.g. Git).
• Understanding of software development methodologies (e.g. Agile).