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Machine Learning Professor Jobs in Texas (NOW HIRING)

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Machine Learning Professor information

What does a machine learning professor do?

A Machine Learning Professor teaches university-level courses on machine learning, data science, and related topics. They conduct original research in the field, often publishing their findings in academic journals and presenting at conferences. In addition to lecturing, they mentor students, supervise theses or dissertations, and may collaborate with industry partners on research projects. Their work helps advance the understanding and applications of machine learning.

What are the key skills and qualifications needed to thrive as a machine learning professor, and why are they important?

To thrive as a Machine Learning Professor, you need deep expertise in machine learning algorithms, mathematics, and data science, typically supported by a Ph.D. in computer science or a related field. Familiarity with programming languages like Python and R, as well as tools such as TensorFlow, PyTorch, and academic research platforms, is essential. Exceptional communication, mentorship abilities, and enthusiasm for teaching help inspire and guide students and peers. These skills ensure effective research output, high-quality teaching, and the development of future leaders in the field.

How does a machine learning professor typically balance research, teaching, and mentorship responsibilities?

Machine Learning Professors usually split their time between conducting original research, teaching undergraduate and graduate courses, and mentoring students. Balancing these responsibilities can be challenging, particularly when managing multiple research projects and supervising student theses. Professors often collaborate with industry partners and other academic departments, which enhances research opportunities but also adds to their workload. Effective time management and prioritization are key to thriving in this dynamic environment, and universities often provide some flexibility in teaching loads or research support to help maintain this balance.
Machine Learning Professor jobs near you
Infographic showing various Machine Learning Professor job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Tenure-Track: Assistant Professor (CHEN, AI and Machine Learning in Chemical Engineering)

College Station, TX • On-site

Texas A&M University
Colleges, Universities, and Professional Schools • 1 - 5K employees
Learn more about Texas A&M Universityexternal

Full-time

Posted 18 days ago

Texas A&M University rating

7.8

Company rating: 7.8 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

Job description

Description
The Artie McFerrin Department of Chemical Engineering, 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 (chenfacultyservices@tamu.edu).

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