Postdoc in Autonomous Materials Discovery - DTU Energy
On-site · Kongens Lyngby, Capital Region, Denmark
Job Summary
Develop computational frameworks for predictive materials synthesis using machine learning interatomic potentials, atomistic simulations, and data-driven workflows; integrate computational predictions with autonomous experimental workflows; collaborate with CAPeX and international partners; disseminate results through scientific publications and conference presentations; engage in collaborative research within CAPeX and associated international collaborations; contribute to development of data-driven optimization and analysis pipelines; participate in dissemination of results through publications and presentations.
Required Qualifications
- A PhD degree in materials science, chemistry, physics, computer science, or a related field
- Experience with atomistic simulations and molecular dynamics
- Experience with machine learning for materials science, chemistry, or physics
- Strong programming skills in Python
- Experience with machine learning, data science, or scientific computing
- Excellent communication skills in spoken and written English
- Experience with one or more of the following areas is advantageous: machine learning interatomic potentials; autonomous laboratories or scientific automation; structural characterisation methods; high-performance computing; generative AI or active learning methods for materials discovery
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