Postdoctoral Fellow - Transmission Electron Microscopy, Texas Materials Institut
Phase2 Technology, Austin, TX, United States
Postdoctoral Fellow - Transmission Electron Microscopy, Texas Materials Institute, Cockrell School of Engineering
flexible benefit account, sick time, tuition assistance, 403(b), retirement plan, employee discount
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
Job Details:
As a top‑10 engineering school with the No. 1 program in Texas, the Cockrell School of Engineering at The University of Texas at Austin has been a global leader in technology innovation and engineering education for over a century. With 11 undergraduate and 13 graduate programs, over 20 research centers and a faculty community that boasts one of the highest numbers of National Academy of Engineering members among U.S. universities, Texas Engineering has launched some of the nation’s most accomplished leaders and pioneered world‑changing solutions in virtually every industry, from space exploration to energy to health care. Situated in the heart of Austin — named “America’s Coolest City” by Expedia and “The Best Place to Live in the U.S.” by U.S. News and World Report — the Cockrell School embodies the city’s innovative spirit. Major companies with Austin campuses, such as Dell, National Instruments, Apple, IBM, Samsung, Google, and many others, continue to recruit Cockrell School students at a remarkable rate, launching thousands of successful careers and developing Texas Engineers into industry leaders.
Purpose
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.
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 modelling.
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.
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.
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume in the first step of the online job application process. Then, any additional Required Materials will be uploaded in the My Experience section; you can multi‑select the additional files or click the Upload button for each file. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find Jobs. Before you apply, log in to Workday, navigate to your Worker Profile, click the Career link in the left‑hand navigation menu, and then update the sections in your Professional Profile. This information will be pulled into your application. The application is one page, and you will need to click the Upload button multiple times to attach your Resume, References and any additional Required Materials noted above.
Employment Eligibility
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
Retirement Plan Eligibility
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
Background Checks
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and 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 in employment, educational programs and activities, and admissions.
Pay Transparency
The University of Texas at Austin will not disclose or otherwise discriminate against employees or applicants who have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
Employment Eligibility Verification
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents must be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
E-Verify
The University of Texas at Austin uses E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
E-Verify Poster (English and Spanish) [PDF]
Right to Work Poster (English) [PDF]
Right to Work Poster (Spanish) [PDF]
Compliance
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP‑3031. The Clery Act requires all prospective employees to be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.
Helping all candidates find great careers is our goal. The information you provide here is secure and confidential.
We are now directing you to the original job posting. Please apply directly for this job at the employer’s website.
#J-18808-Ljbffr
flexible benefit account, sick time, tuition assistance, 403(b), retirement plan, employee discount
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
Job Details:
As a top‑10 engineering school with the No. 1 program in Texas, the Cockrell School of Engineering at The University of Texas at Austin has been a global leader in technology innovation and engineering education for over a century. With 11 undergraduate and 13 graduate programs, over 20 research centers and a faculty community that boasts one of the highest numbers of National Academy of Engineering members among U.S. universities, Texas Engineering has launched some of the nation’s most accomplished leaders and pioneered world‑changing solutions in virtually every industry, from space exploration to energy to health care. Situated in the heart of Austin — named “America’s Coolest City” by Expedia and “The Best Place to Live in the U.S.” by U.S. News and World Report — the Cockrell School embodies the city’s innovative spirit. Major companies with Austin campuses, such as Dell, National Instruments, Apple, IBM, Samsung, Google, and many others, continue to recruit Cockrell School students at a remarkable rate, launching thousands of successful careers and developing Texas Engineers into industry leaders.
Purpose
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.
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 modelling.
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.
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.
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume in the first step of the online job application process. Then, any additional Required Materials will be uploaded in the My Experience section; you can multi‑select the additional files or click the Upload button for each file. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find Jobs. Before you apply, log in to Workday, navigate to your Worker Profile, click the Career link in the left‑hand navigation menu, and then update the sections in your Professional Profile. This information will be pulled into your application. The application is one page, and you will need to click the Upload button multiple times to attach your Resume, References and any additional Required Materials noted above.
Employment Eligibility
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
Retirement Plan Eligibility
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
Background Checks
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and 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 in employment, educational programs and activities, and admissions.
Pay Transparency
The University of Texas at Austin will not disclose or otherwise discriminate against employees or applicants who have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
Employment Eligibility Verification
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents must be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
E-Verify
The University of Texas at Austin uses E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
E-Verify Poster (English and Spanish) [PDF]
Right to Work Poster (English) [PDF]
Right to Work Poster (Spanish) [PDF]
Compliance
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP‑3031. The Clery Act requires all prospective employees to be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.
Helping all candidates find great careers is our goal. The information you provide here is secure and confidential.
We are now directing you to the original job posting. Please apply directly for this job at the employer’s website.
#J-18808-Ljbffr