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

The University of Texas at Austin; Austin (TX); On-site; USD 54,983 - 67,202; Full time

Job summary

A leading educational institution located in Austin, Texas, is seeking a Postdoctoral Fellow to advance AI-driven transmission electron microscopy research. This role involves developing intelligent systems integrating machine learning and robotic control. Responsibilities include designing experiments, publishing high-impact research, and mentoring. Ideal candidates hold a Ph.D. and have a strong publication record along with independent research experience. The position offers a competitive salary of $61,093 and requires collaboration in a dynamic research environment.

Qualifications

• Demonstrated experience conducting independent research in a relevant area.
• Strong publication record in peer-reviewed journals.
• Commitment to mentoring graduate and undergraduate students.

Responsibilities

• Develop and implement self-driving TEM workflows for autonomous imaging.
• Advance AI-assisted image interpretation including atomic structure recognition.
• Mentor graduate students and research staff.

Skills

Independent research in materials science or engineering

Collaboration in interdisciplinary environments

Excellent written and verbal communication

Education

Ph.D. in Materials Science, Engineering, Physics, Chemistry, or closely related field

Job description

Overview

Postdoctoral Fellow - Transmission Electron Microscopy, Texas Materials Institute, Cockrell School of Engineering at The University of Texas at Austin. 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). This position focuses on developing 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 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 help establish TMI’s AI-integrated microscopy hub as a national leader in autonomous TEM.

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.

Required Qualifications

• 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

Preferred Qualifications

• None

Salary

$61,093

Working Conditions

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

Required Materials

• Resume/CV
• Letter of interest
• 3 work references with their contact information; at least one reference should be from a supervisor

Employment Eligibility and Compliance

• Employment Eligibility: You must meet all required qualifications and be able to perform essential functions with or without a reasonable accommodation.
• Background Checks: A criminal history background check will be required for finalist(s) under consideration.
• Equal Opportunity Employer: The University of Texas at Austin is an equal opportunity/affirmative action employer. It does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status.
• Pay Transparency: UT Austin will not discriminate against employees or applicants for discussing pay or pay inquiries, with certain restrictions as required by law.
• Employment Verification: If hired, you will complete the federal I-9 form and provide documents proving identity and eligibility to work in the United States.