Postdoctoral Researcher in Autonomous Materials Discovery

Job summary

This position focuses on advancing autonomous materials discovery through the integration of materials chemistry, machine learning, and atomistic simulations. The role involves developing AI-driven methodologies to enhance the synthesis and characterization of advanced materials for sustainable energy applications.

Qualifications

• PhD in materials science, chemistry, physics, computer science, or a related field.
• Experience with atomistic simulations and molecular dynamics.
• Proficiency in machine learning applications relevant to materials science.
• Strong programming skills in Python.
• Excellent communication skills in English.

Responsibilities

• Develop and implement machine learning interatomic potentials for materials chemistry.
• Conduct atomistic simulations and molecular dynamics across various materials systems.
• Integrate computational predictions with autonomous experimental workflows.
• Contribute to data-driven optimization and analysis pipelines.
• Collaborate on research projects within the Pioneer Center and with international partners.
• Publish findings in scientific journals and present at conferences.

Skills

• Experience with machine learning, data science, or scientific computing.
• Familiarity with machine learning interatomic potentials, autonomous laboratories, structural characterization methods, high-performance computing, and generative AI is advantageous but not required.

Education

• A formal qualification of a PhD degree (or equivalent) is mandatory.

Tools

• Python programming language and relevant computational tools for simulations and data analysis.