Postdoc - Theory-in-the-loop of Autonomous Experiments for Materials-by-Design
Oak Ridge, TN, US, 37830
USD 115K-143K (estimate) Mid-level Full Time
Tasks
- Collaborate across interdisciplinary teams and research themes
- Design develop and validate physics informed AI ML models using electronic structure spectroscopy features to control materials growth
- Develop AI ML approaches to bridge length and time scales in simulations
- Develop HPC workflows for multi fidelity simulations to predict and interpret structural and electronic characterization techniques
- Develop and train agentic AI tools to control simulation and or experiments
- Develop physics informed AI ML surrogate models for inverse design of new materials and processes using multi modal datasets
- Ensure environment safety health and quality compliance
- Present and report research results and publish in peer reviewed journals
Perks/Benefits
- Educational assistance
- Flexible work hours
- Generous vacation and holidays
- Medical plans
- On-site fitness
- Parental leave
- Relocation assistance
- Retirement plans
Skills/Tech-stack
Ab Initio | Ab initio methods | Agentic AI | Artificial Intelligence | Automation | Deep learning | Diagram modeling | Force Field | Force Field Modeling | High Performance | High-Performance Computing | Inverse design | Kinetic Monte Carlo | Machine Learning | Materials discovery | Monte Carlo | Multimodal Data | Performance Computing | Phase Diagram Modeling | Phase Field Modeling | Phase-field | Physics-Based Modeling | Physics-informed machine learning | Quantum materials | Surrogate modeling | Tight Binding
Education
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