Postdoctoral Fellow - TMI, Agentic AI, Texas Materials Institute, Cockrell Schoo
Phase2 Technology, Austin, TX, United States
Postdoctoral Fellow - TMI, Agentic AI, Texas Materials Institute, Cockrell School of Engineering
Hiring Department:
Walker Department of Mechanical Engineering
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
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 centers 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". Responsibilities include building systems that can interpret multimodal data streams, interface with instrument control systems, and autonomously execute experimental tasks with minimal human intervention.
The role is embedded within TMI's larger AI-robotic materials discovery program and collaborates closely with researchers in synthesis, characterization, and computational science. The fellow will contribute to establishing a continuous experimental-computational feedback loop, publish independent research, and mentor graduate students and junior researchers.
Responsibilities
Develop agentic AI models and 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 for autonomous operation across diverse scientific instruments.
Build and refine digital twins for synthesis and characterization workflows using physics‑based simulations and/or surrogate machine learning models.
Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners.
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.
Perform other related duties as assigned.
Required Qualifications
Ph.D. in Materials Science, Computer Science, Engineering, Applied Physics, or a closely related field, conferred within three (3) years before the start date.
Strong proficiency in Python and modern machine learning 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 and conferences.
Excellent written and verbal communication skills.
Ability to work collaboratively in an interdisciplinary research environment and comfort working with real‑world experimental conditions.
Commitment to mentoring and contributing to the academic development of graduate and undergraduate students.
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 contact information; at least one reference should be from a supervisor.
Additional Information
Employment Eligibility: Please ensure you meet all required qualifications and can perform all essential functions with or without reasonable accommodation.
Retirement Plan Eligibility: Teacher Retirement System of Texas (TRS) for positions at least 20 hours per week and 135 days in length; optional retirement program available for 40‑hour positions with 135 days.
Background Checks: A criminal history background check will be required for finalists.
Equal Opportunity Employer
The University of Texas at Austin complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to 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.
Pay Transparency
The University of Texas at Austin will not discriminate against employees or applicants who inquire about or disclose pay. Employees who have access to compensation information cannot disclose pay to individuals without such access unless required by law or investigation.
E-Verify
The University of Texas at Austin uses E-Verify to check work authorization. For more information,
see the E-Verify website.
Compliance
Employees may be required to report violations of Title IX and the Clery Act. All prospective employees should be notified of the availability of the Annual Security and Fire Safety report. The Clery Act requires access to the report, which may be obtained from University Compliance Services.
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Hiring Department:
Walker Department of Mechanical Engineering
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
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 centers 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". Responsibilities include building systems that can interpret multimodal data streams, interface with instrument control systems, and autonomously execute experimental tasks with minimal human intervention.
The role is embedded within TMI's larger AI-robotic materials discovery program and collaborates closely with researchers in synthesis, characterization, and computational science. The fellow will contribute to establishing a continuous experimental-computational feedback loop, publish independent research, and mentor graduate students and junior researchers.
Responsibilities
Develop agentic AI models and 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 for autonomous operation across diverse scientific instruments.
Build and refine digital twins for synthesis and characterization workflows using physics‑based simulations and/or surrogate machine learning models.
Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners.
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.
Perform other related duties as assigned.
Required Qualifications
Ph.D. in Materials Science, Computer Science, Engineering, Applied Physics, or a closely related field, conferred within three (3) years before the start date.
Strong proficiency in Python and modern machine learning 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 and conferences.
Excellent written and verbal communication skills.
Ability to work collaboratively in an interdisciplinary research environment and comfort working with real‑world experimental conditions.
Commitment to mentoring and contributing to the academic development of graduate and undergraduate students.
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 contact information; at least one reference should be from a supervisor.
Additional Information
Employment Eligibility: Please ensure you meet all required qualifications and can perform all essential functions with or without reasonable accommodation.
Retirement Plan Eligibility: Teacher Retirement System of Texas (TRS) for positions at least 20 hours per week and 135 days in length; optional retirement program available for 40‑hour positions with 135 days.
Background Checks: A criminal history background check will be required for finalists.
Equal Opportunity Employer
The University of Texas at Austin complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to 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.
Pay Transparency
The University of Texas at Austin will not discriminate against employees or applicants who inquire about or disclose pay. Employees who have access to compensation information cannot disclose pay to individuals without such access unless required by law or investigation.
E-Verify
The University of Texas at Austin uses E-Verify to check work authorization. For more information,
see the E-Verify website.
Compliance
Employees may be required to report violations of Title IX and the Clery Act. All prospective employees should be notified of the availability of the Annual Security and Fire Safety report. The Clery Act requires access to the report, which may be obtained from University Compliance Services.
#J-18808-Ljbffr