The Postdoctoral Fellow will lead research in agentic AI and autonomous laboratory systems as part of the advanced materials characterization and discovery initiatives within the Texas Materials Institute (TMI) at The University of Texas at Austin. This position is centered on developing AI agents and agentic orchestration frameworks capable of observing, reasoning, planning, and acting across multiple classes of scientific instruments, enabling a fully integrated closed-loop “self-driving laboratory”. The role focuses on providing the AI core and foundational intelligence that coordinates the entire experimental ecosystem.
The Postdoctoral Fellow will develop agentic AI models and frameworks for intelligent experimental workflows, which couple machine learning, real-time data analysis, and tool-use APIs to automate complex decision-making across materials design, liquid-phase synthesis, and characterization platforms. Responsibilities include building agentic AI systems that can interpret multimodal data streams, interface with instrument control systems, and autonomously execute experimental tasks with minimal human intervention. The Postdoctoral Fellow will be responsible for both developing and deploying agentic AI systems in real laboratory environments, ensuring robust performance under real-world noise, uncertainty, and instrument variability.
This position is embedded within TMI's larger AI-robotic materials discovery program, which integrates liquid-phase synthesis, high-throughput sample processing, and autonomous characterization. The Postdoctoral Fellow will collaborate closely with companion researchers in synthesis and characterization and will provide the foundational AI layer that enables these autonomous workflows. The agentic AI systems developed through this position are expected to interface seamlessly with high-throughput deposition tools, robotic sample-handling infrastructure, and adaptive characterization routines. Working within this highly integrated environment, the Postdoctoral Fellow will contribute to establishing a continuous experimental–computational feedback loop, in which real-time measurements feed directly into AI reasoning layers and digital twins, and autonomous agents determine the next experiments needed for accelerated materials discovery.
The emphasis of this position is the creation of system-level intelligent automation, where multiple scientific instruments are coordinated through a unified agentic AI framework. The Postdoctoral Fellow will be expected to lead and publish independent research, collaborate across disciplines of computer science, materials science, electron microscopy, chemistry, and data science, and contribute to the mentorship of graduate students and junior researchers. Additional responsibilities include developing new protocols for experimental AI agents, contributing to multi-PI proposals, and helping define the architecture for fully autonomous electron microscopy systems. This position offers a unique opportunity to be at the forefront of AI-driven discovery science, operating at the interface between robotic automation, advanced electron microscopy, and intelligent materials design.
$61,093
Tagged as: Life Sciences
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