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

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

Hiring Department: Walker Department of Mechanical Engineering

Position Open To: All Applicants

Weekly Scheduled

Hours:

40

FLSA Status: Exempt

Earliest

Start Date:

Immediately

Position Duration: Expected to Continue Until Aug 31, 2027

Location: UT MAIN CAMPUS

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 centers 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". Responsibilities include building systems that can interpret multimodal data streams, interface with instrument control systems, and autonomously execute experimental tasks with minimal human intervention.

The role is embedded within TMI's larger AI-robotic materials discovery program and collaborates closely with researchers in synthesis, characterization, and computational science. The fellow will contribute to establishing a continuous experimental-computational feedback loop, publish independent research, and mentor graduate students and junior researchers.

• Develop agentic AI models and 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 for autonomous operation across diverse scientific instruments.
• Build and refine digital twins for synthesis and characterization workflows using physics‑based simulations and/or surrogate machine learning models.
• Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners.
• 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.
• 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.
• Strong proficiency in Python and modern machine learning 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 and comfort working with real‑world experimental conditions.
• 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 of interest
• 3 work references with contact information; at least one reference should be from a supervisor.

Employment Eligibility:
Please ensure you meet all required qualifications and can perform all essential functions with or without reasonable accommodation.

Retirement Plan Eligibility:
Teacher Retirement System of Texas (TRS) for positions at least 20 hours per week and 135 days in length; optional…