
Tenure-Track: Assistant Professor (CHEN, AI and Machine Learning in Chemical Engineering)
Texas A&M University - Faculty
College Station, TXFull-timeSeen 1w agoSeen in employer's feed 1w ago
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Job overview
The Artie McFerrin Department of Chemical Engineering at Texas A&M University seeks a full‑time, tenure‑track Assistant Professor to lead AI‑driven research at the intersection of computational data science and chemical engineering, teach graduate and undergraduate courses, mentor students, and contribute service to the department and profession beginning Fall 2027.
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Full job description
Tenure-Track: Assistant Professor (CHEN, AI and Machine Learning in Chemical Engineering) Chemical Engineering
Location College Station, TX
Open Date 8/26/2026
Position ID 190610
Description
The Artie McFerrin Department of Chemical Engineering (https://engineering.tamu.edu/chemical/index.html) , College of Engineering at Texas A&M University invites applications for a full-time, tenure-track, assistant professor position with a 9-month academic appointment and the possibility of an additional summer appointment contingent upon need and availability of funds, beginning Fall 2027.
The principal focus for this position is at the intersection of computational data science and Chemical Engineering. We are particularly interested in candidates whose research leverages Artificial Intelligence (AI) and Machine Learning (ML) to advance process systems design, process safety and risk analysis, optimization, characterization, and predictive modeling of materials.
The successful candidate is expected to establish and sustain a nationally recognized, externally funded research program that integrates computational data science with chemical engineering. Areas of emphasis may include, but are not limited to, data-driven process modeling and simulation of complex, large-scale systems; AI-accelerated materials design, including structure-property relationships, self-assembly, and processing optimization; machine learning-driven characterization and stabilization of colloidal systems; physics-informed machine learning; generative models for materials discovery; advanced ML and AI methods for process design, safety and risk analysis, and high-throughput computational screening methodologies addressing fundamental and applied challenges in chemical engineering.
The successful candidate will be expected to conduct original, scholary research; build self-sustaining research programs; teach both graduate and undergraduate courses and mentor students; and contribute an appropriate degree of service to the Department, College, University, and profession.
Candidates must demonstrate a strong commitment to excellence in teaching and mentoring at both undergraduate and graduate levels, as well as active engagement in departmental, college, and professional service. The ideal candidate will articulate a clear vision for integrating experimental, theoretical, and data-driven approaches to solve complex problems at the forefront of chemical engineering research.
Qualifications
Applicants must hold a Ph.D. in Chemical Engineering or a closely related field, with an outstanding record of scholarly achievement, the ability to secure competitive research funding, and evidence of effective teaching and mentorship.
Application Instructions
Applicants must submit a cover letter, curriculum vitae, a personal Statement (your statement should include your philosophy and plans for research, teaching, and service as applicable), and a list of four contact references (including postal addresses, phone numbers and email addresses) by applying for this specific position at https://apply.interfolio.com/192372. Full consideration will be given to applications received by December 15, 2026. Applications received after that date may be considered until the position is filled. It is anticipated the appointment will begin in Fall 2027.
For questions regarding the application process or other inquiries, please contact Mr. Mateo Andres ([email protected]).
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