Postdoctoral Fellow - TMI, Agentic AI, Texas Materials Institute, Cockrell School of Engineering

Postdoctoral Fellow – TMI, Agentic AI, Texas Materials Institute, Cockrell School of Engineering

Location:

UT Main Campus
•

Start Date:

Immediately
• Duration:
Until Aug 31, 2027
• Weekly

Hours:

40
• Position Type:
Exempt (Full‑time)

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  role focuses on developing AI agents and agentic orchestration frameworks capable of observing, reasoning, planning, and acting across multiple scientific instruments to enable a fully integrated closed‑loop “self‑driving laboratory”.

The Fellow will provide the AI core and foundational intelligence that coordinates the entire experimental ecosystem, integrating machine learning, real‑time data analysis, and tool‑use APIs to automate complex decision‑making across materials design, liquid‑phase synthesis, and characterization platforms. 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 Fellow will collaborate closely with synthesis and characterization researchers and provide the foundational AI layer that enables these autonomous workflows, creating a continuous experimental–computational feedback loop.

The emphasis is on system‑level intelligent automation, coordinating multiple instruments through a unified agentic AI framework. The Fellow will lead independent research, publish findings, collaborate across disciplines, and mentor graduate students and junior researchers.

• Develop agentic AI models and agentic orchestration frameworks for multi‑step, multi‑instrument experimental workflows (e.g., observe–reason–plan–act).
• Design closed‑loop optimization and active learning strategies for real‑time experiment steering and adaptive decision‑making.
• Integrate agentic AI systems with instrument control APIs, laboratory scheduling systems, and data acquisition interfaces to enable autonomous operation across diverse scientific instruments.
• Build and refine digital twins for synthesis and characterization workflows, using physics‑based simulations or surrogate ML models.
• Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners, including postdoctoral fellows in liquid‑phase synthesis, microdroplet printing, and characterization.
• Publish high‑impact research, present findings at international conferences, and contribute to proposal development for new initiatives in agentic AI and autonomous laboratory systems.
• Mentor graduate students and research staff, fostering interdisciplinary collaboration between materials science, data science, and robotics.
• Collaborate with the Texas Materials Institute’s instrumentation and AI engineering teams to help define the architecture for next‑generation autonomous materials research laboratories at UT Austin.
• Perform other related duties as assigned.

• Ph.D. in Materials Science, Computer Science, Engineering, Applied Physics, or a closely related field, conferred within three (3) years before the start date of the appointment.
• Strong proficiency in Python and modern ML and agentic AI frameworks.
• Experience with control, optimization, or reinforcement learning, or workflow automation / multi‑agent systems.
• Demonstrated experience conducting independent research in a relevant area of materials science or engineering.
• Strong publication record in peer‑reviewed journals and conferences.
• Excellent written and verbal communication skills.
• Ability to work collaboratively in an interdisciplinary research environment, comfortable handling real‑world experimental uncertainty, noise, and incomplete data.
• Commitment to mentoring and contributing to the academic development of graduate and undergraduate students.

$61,093

• May work around standard office conditions.
• Repetitive use of a keyboard at a workstation.
• Use of manual dexterity.

• Resume/CV.
• Letter…