Postdoctoral Fellow - TMI, Agentic AI, Texas Materials ...

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 TMIs 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 experimentalcomputational 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.ResponsibilitiesDevelop agentic AI models and agentic orchestration frameworks for multi-step, multi-instrument experimental workflows (e.g., observereasonplanact). Design closed-loop optimization and active learning strategies for real-time experiment steering and adaptive decision-makingIntegrate agentic AI systems with instrument control APIs, laboratory scheduling systems, and data acquisition interfaces, enabling autonomous operation across diverse scientific instrumentsBuild and refine digital twins for synthesis and characterization workflows, using physics-based simulations and/or surrogate ML modelsCollaborate closely with experimentalists, theorists, and engineers across academic and industrial partners. Work with postdoctoral fellows in liquid-phase synthesis, microdroplet printing, and characterizationPublish high-impact research, present findings at international conferences, and contribute to proposal development for new initiatives in agentic AI and autonomous laboratory systemsMentor graduate students and research staff, fostering interdisciplinary collaboration between materials science, data science, and roboticsCollaborate with the Texas Materials Institutes instrumentation and AI engineering teams to help define the architecture for next-generation autonomous materials research laboratories at UT AustinPerforms other related duties as assigned

Created: 2025-12-05