The University of Texas at Austin
Postdoctoral Fellow - TMI, Agentic AI, Texas Materials Institute, Cockrell Schoo
The University of Texas at Austin, Austin, Texas, us, 78716
Postdoctoral Fellow - TMI, Agentic AI, Texas Materials Institute, Cockrell School of Engineering
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 is centered 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”. The role focuses on providing the AI core and foundational intelligence that coordinates the entire experimental ecosystem. The Postdoctoral Fellow will develop agentic AI models and frameworks for intelligent experimental workflows, which couple machine learning, real-time data analysis, and tool‑use APIs to automate complex decision‑making across materials design, liquid‑phase synthesis, and characterization platforms. Responsibilities include building agentic AI systems that can interpret multimodal data streams, interface with instrument control systems, and autonomously execute experimental tasks with minimal human intervention. The Postdoctoral Fellow will be responsible for both developing and deploying agentic AI systems in real laboratory environments, ensuring robust performance under real‑world noise, uncertainty, and instrument variability. This position is embedded within TMI’s larger AI‑robotic materials discovery program, which integrates liquid‑phase synthesis, high‑throughput sample processing, and autonomous characterization. The Postdoctoral Fellow will collaborate closely with companion researchers in synthesis and characterization and will provide the foundational AI layer that enables these autonomous workflows. The agentic AI systems developed through this position are expected to interface seamlessly with high‑throughput deposition tools, robotic sample‑handling infrastructure, and adaptive characterization routines. Working within this highly integrated environment, the Postdoctoral Fellow will contribute to establishing a continuous experimental–computational feedback loop, in which real‑time measurements feed directly into AI reasoning layers and digital twins, and autonomous agents determine the next experiments needed for accelerated materials discovery. The emphasis of this position is the creation of system‑level intelligent automation, where multiple scientific instruments are coordinated through a unified agentic AI framework. The Postdoctoral Fellow will be expected to lead and publish independent research, collaborate across disciplines of computer science, materials science, electron microscopy, chemistry, and data science, and contribute to the mentorship of graduate students and junior researchers. Additional responsibilities include developing new protocols for experimental AI agents, 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.
Benefits UT Austin provides outstanding employee benefits and total rewards packages that include:
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
Responsibilities
Develop agentic AI models and agentic 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, enabling autonomous operation across diverse scientific instruments
Build and refine digital twins for synthesis and characterization workflows, using physics-based simulations and/or surrogate ML models
Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners. Work with postdoctoral fellows in liquid-phase synthesis, microdroplet printing, and characterization
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 at UT Austin
Performs 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 of the appointment
Strong proficiency in Python and modern ML 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/conferences
Excellent written and verbal communication skills
Ability to work collaboratively in an interdisciplinary research environment. Comfort working with real-world experimental environments, including handling uncertainty, noise, and incomplete data
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 their contact information; at least one reference should be from a supervisor
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 discharge or in any other manner discriminate against employees or applicants because they 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 a 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 need to 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
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) and may be required to report if this position is identified as a Campus Security Authority. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees 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.
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Benefits UT Austin provides outstanding employee benefits and total rewards packages that include:
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
Responsibilities
Develop agentic AI models and agentic 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, enabling autonomous operation across diverse scientific instruments
Build and refine digital twins for synthesis and characterization workflows, using physics-based simulations and/or surrogate ML models
Collaborate closely with experimentalists, theorists, and engineers across academic and industrial partners. Work with postdoctoral fellows in liquid-phase synthesis, microdroplet printing, and characterization
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 at UT Austin
Performs 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 of the appointment
Strong proficiency in Python and modern ML 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/conferences
Excellent written and verbal communication skills
Ability to work collaboratively in an interdisciplinary research environment. Comfort working with real-world experimental environments, including handling uncertainty, noise, and incomplete data
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 their contact information; at least one reference should be from a supervisor
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 discharge or in any other manner discriminate against employees or applicants because they 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 a 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 need to 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
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) and may be required to report if this position is identified as a Campus Security Authority. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees 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.
#J-18808-Ljbffr