Postdoctoral Fellow (AI/ML-driven High-throughput Material Synthesis and Characterization)
Core
Develop and implement AI/ML-driven high-throughput, closed-loop experimentation for the discovery and optimization of electrocatalytic materials for CO₂ reduction.
Role type
Postdoctoral Fellow (AI/ML-driven materials synthesis and characterization)
Builds
Automated materials fabrication and characterization workflows with agentic AI systems
Domain
Materials Science / Electrochemistry / Artificial Intelligence
Deliverable
production ML models
Required skills
AI/ML-driven high-throughput experimentation, agentic AI workflow development, multi-parameter optimization, structure–performance analysis, machine learning techniques, data-driven approaches
Preferred skills
Experience in electrochemistry, autonomous experimentation, in-situ catalyst regeneration
Technologies
AI agents, optimization algorithms, data-driven platforms
Responsibilities
Design automated workflows integrating AI for autonomous experimentation, optimize experimental process spaces, validate predictive models with computational researchers, analyze structure–performance relationships, publish research articles
Seniority
Postdoctoral, independent researcher
