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

University of Texas at Austin • Austin, TX

37d • Onsite

About The Position

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.

Requirements

• 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/conferences
• Excellent written and verbal communication skills
• Ability to work collaboratively in an interdisciplinary research environment. Comfort working with real-world experimental environments, including handling uncertainty, noise, and incomplete data
• Commitment to mentoring and contributing to the academic development of graduate and undergraduate students

Responsibilities

• 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, enabling autonomous operation across diverse scientific instruments
• Build and refine digital twins for synthesis and characterization workflows, using physics-based simulations and/or surrogate ML models
• Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners. Work with 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
• Performs other related duties as assigned

Benefits

• Competitive health benefits (employee premiums covered at 100%, family premiums at 50%)
• Voluntary Vision, Dental, Life, and Disability insurance options
• Generous paid vacation, sick time, and holidays
• Teachers Retirement System of Texas, a defined benefit retirement plan, with 7.75% employer matching funds
• Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b)
• Flexible spending account options for medical and childcare expenses
• Robust free training access through LinkedIn Learning plus professional conference opportunities
• Tuition assistance
• Expansive employee discount program including athletic tickets
• Free access to UT Austin's libraries and museums with staff ID card
• Free rides on all UT Shuttle and Austin CapMetro buses with staff ID card

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Educational Services
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees