Postdoctoral Fellow - Transmission Electron Microscopy, Texas Materials Institute, Cockrell School of Engineering

The University of Texas at Austin • Austin, UT

1d • $61,093 • Onsite

About The Position

The Postdoctoral Fellow will lead research in AI-driven and self-driving transmission electron microscopy (TEM) as part of the advanced materials characterization and autonomous discovery initiatives within the Texas Materials Institute (TMI) at The University of Texas at Austin. This position focuses on developing the next generation of intelligent electron microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and execute experiments that advance the frontier of self-optimizing microscopy, including automated alignment, adaptive focusing, drift correction, and AI-assisted atomic structure recognition. The role involves building and training deep-learning models for TEM image reconstruction and interpretation, linking image features to local chemistry, defects, and dynamic transformations under varying environmental or beam conditions. The successful candidate will work closely with faculty and research staff to help establish TMI’s new AI-integrated microscopy hub as a national leader in self-driving electron microscopy. This position will operate as a core part of a larger AI-robotic materials discovery program at TMI that couples liquid-phase synthesis, high-throughput sample processing, and autonomous characterization. The TEM postdoctoral fellow will collaborate with companion postdocs specializing in liquid-phase synthesis and thin-film formation, integrating structural characterization directly with compositional and processing data streams. A central aspect of this work will be to interface TEM workflows with the microdroplet printing system for high-throughput sample deposition onto TEM grids or micro-electrode arrays, enabling statistically rich structure–property correlations across thousands of printed materials. The Fellow will also contribute to the development of robotic sample preparation and automated sample-loading systems, including a robot arm-based TEM grid handling and holder-loading setup, to achieve continuous, unsupervised operation of the microscope. Working within this integrated ecosystem, the Fellow will help connect real-time TEM data to cloud-based digital twins and the broader AI framework controlling synthesis and electrochemical testing, creating a closed experimental–computational feedback loop for autonomous materials discovery. The Postdoctoral Fellow will be expected to lead and publish independent research, collaborate across disciplines of materials science, electron microscopy, chemistry, and data science, and contribute to the mentorship of graduate students and junior researchers. Additional responsibilities include developing new experimental protocols, 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, Engineering, Physics, Chemistry, or a closely related field, conferred within three (3) years before the start date of the appointment
• Demonstrated experience conducting independent research in a relevant area of materials science or engineering
• Strong publication record in peer-reviewed journals
• Excellent written and verbal communication skills
• Ability to work collaboratively in an interdisciplinary research environment
• Commitment to mentoring and contributing to the academic development of graduate and undergraduate students

Responsibilities

• Develop and implement self-driving TEM workflows that integrate machine learning, computer vision, and automated microscope control for autonomous imaging, focusing, and data acquisition.
• Advance AI-assisted image interpretation, including atomic structure recognition, defect classification, and dynamic process tracking using deep-learning and physics-informed models.
• Integrate TEM operations with robotic sample handling, including the design, testing, and deployment of a robot-arm–based TEM grid-loading and exchange system for continuous, unattended operation.
• Collaborate with postdoctoral fellows in liquid-phase synthesis and microdroplet printing to establish seamless sample transfer pipelines from synthesis to TEM analysis, enabling high-throughput, correlative characterization.
• Develop and optimize sample preparation methods compatible with microdroplet-printed thin films, nanoparticle arrays, and electrochemical catalyst systems, ensuring reproducible and contamination-free data.
• Link real-time TEM data streams to digital twin and AI platforms, using cloud-based computation for adaptive experiment control, hypothesis generation, and structure–property modeling.
• Publish high-impact research, present findings at international conferences, and contribute to proposal development for new AI-in-microscopy and autonomous discovery initiatives.
• 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 TEM facilities 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: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees