Postdoctoral Fellow - Transmission Electron Microscopy, Texas Materials Institute, Cockrell Sch

Job Posting

Title:

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

Hiring Department: Texas Materials Institute

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 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  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.

Working as a core part of a larger AI‑robotic materials discovery program, the Fellow will collaborate with peer postdocs specializing in liquid‑phase synthesis and thin‑film formation, and integrate TEM workflows with the micro‑droplet 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 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.

By connecting real‑time TEM data to cloud‑based digital twins and broader AI frameworks, the Fellow will help create a closed experimental‑computational feedback loop for autonomous materials discovery.

• 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 micro‑droplet 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 micro‑droplet‑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.
• Perform other related duties as assigned.

• 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…