Prairie View A&M University

Postdoctoral Researcher - Artificial Intelligence

USA new gradAI/ML/DataPosted 24 days ago

Location: Prairie View, TX
Type: USA new grad

About this role

From Prairie View A&M University’s careers page.

Job Title

Postdoctoral Researcher (Artificial Intelligence)

Agency

Prairie View A&M University

Department

Department Of Computer Science

Proposed Minimum Salary

Commensurate

Job Location

Prairie View, Texas

Job Type

Staff

Job Description

Important Immigration information:

A Presidential proclamation issued on September 19, 2025, imposes a $100,000 fee on new H-1B petitions filed after September 21, 2025. Please be advised that Texas A&M University will NOT pay this fee. Therefore, if you need immigration sponsorship for your employment, we recommend that you consult with your private immigration counsel at your own expense to ascertain whether your current immigration status would make a potential offer of employment from Texas A&M University subject to this fee.

In addition, on January 27, 2026, Texas Governor Abbot issued a moratorium on the filing of any new H-1B unless approved by the Texas Workforce Commission. Accordingly, if you will now or in the future require sponsorship for employment visa status this moratorium may affect our ability to employ you should you be selected as the final candidate.

_____________________________________________________________________________

We invite applications for a highly motivated Postdoctoral Researcher to join an interdisciplinary research team advancing artificial intelligence and machine learning for metal additive manufacturing. The position will support Advanced Manufacturing research projects focused on Electron Beam Powder Bed Fusion (E-PBF) of refractory alloys, with emphasis on in-situ backscattered electron (BSE) microstructure sensing, process microstructure modeling, and AI enabled closed-loop process control. The research will also incorporate X-ray computed tomography (XCT) image processing and analysis to characterize internal defects and provide complementary validation of machine learning predictions and in-situ monitoring results.

The successful candidate will contribute to data engineering, scientific image and sequence analysis, deep learning model development, benchmarking, real-time prediction and control, reproducible research software, and scientific communication. The position also includes opportunities for research leadership, student mentoring, proposal development, manuscript preparation, and collaborative work with engineering and materials-science researchers. Student supervision, method development, and major research-direction decisions will be carried out in close coordination with the PI and the project team.

This position is funded by restricted or grant funds. Continued employment is contingent upon the availability and renewal of restricted or grant funding.

The salary is determined in accordance with the University’s compensation structure and will be commensurate with the candidates’ education and experience, within the assigned salary range for this position.

Responsibilities:

Develop, train, validate, and optimize machine learning and deep learning models for process microstructure prediction using E-PBF process parameters and time-resolved in-situ BSE data, with particular emphasis on transformer-based and other sequence-learning architectures, in coordination with the PI and project objectives.
Process, clean, curate, align, and analyze high-resolution image and process datasets; develop reproducible pipelines for data quality assessment, feature extraction, labeling, and model-ready dataset generation.
Develop and evaluate methods that connect scan strategies, process parameters, in-situ observations, and resulting microstructural features, including assessment of model performance, uncertainty, repeatability, and generalization, in consultation with the PI.
Contribute to real-time AI-enabled closed-loop control by developing prediction and control approaches that can inform or adapt E-PBF scan strategies based on sensor feedback, in alignment with the PI’s research direction and project goals.
Integrate machine learning components with data-acquisition and control workflows in collaboration with experimental researchers and engineering collaborators.
Maintain clear documentation of datasets, model architecture, software, computational workflows, experiments, and results, and contribute to a well-organized research codebase and repository.
Provide leadership within the research group by taking ownership of project components and coordinating research tasks with collaborators.
Mentor and assist with the supervision of undergraduate and graduate students in machine learning, data analysis, scientific computing, research methods, and technical communication, in coordination with the PI.
Train new group members on computational tools, reproducible machine learning workflows, and research best practices.
Assist the principal investigator with manuscripts, research proposals, technical reports, presentations, and other scholarly outputs.
Performs other duties as assigned.

Required Education and Experience:

Ph.D. in Computer Science, Electrical or Computer Engineering, Mechanical Engineering, Materials Science and Engineering, Data Science, Applied Mathematics, or a closely related discipline.
At least one year of research experience applying machine learning, deep learning, computer vision, scientific data analytics, or related data-driven methods.

Required Knowledge, Skills, and Abilities:

Strong programming skills in Python and experience with machine learning libraries such as PyTorch, TensorFlow, scikit learn, and Keras.
Knowledge of machine learning, deep learning, data analytics, and model evaluation.
Experience with data processing pipelines, statistical analysis, and data visualization tools including matplotlib, seaborn, and Plotly.
Strong written and verbal communication abilities and the ability to collaborate with multidisciplinary teams.
A record of contributing to peer reviewed publications.

Job Posting Close Date:

Until Filled

Required Attachments:

Please attach all required documents listed below in the attachment box labeled as either “Resume/CV or Resume/Cover Letter” on the application. Multiple attachments may be included in the “Resume/CV” or Resume/Cover Letter” attachment box. Any additional attachments provided outside of the required documents listed below are considered optional.

Resume or Curriculum Vitae
Cover Letter

Application Submission Guidelines:

All applicants are required to apply via our Career Site on or before the closing date indicated on the job posting. Applicant inquiries received via email and websites such as Indeed, HigherEdJobs, etc. will not be considered unless the individual has applied to the available position via the PVAMU Career site.

The required documents listed in the above "Required Attachments" section must be attached to the application prior to the job closing date indicated to ensure full consideration for the application submitted. Please contact the Office of Human Resource on or before the closing date indicated above at 936-261-1730 or jobs@pvamu.edu should you need assistance with the online application process.

Background Check Requirements:

All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution’s verification of credentials and/or other information required by the institution’s procedures, including the completion of the criminal history check.

Equal Opportunity/Veterans/Disability Employer.

Apply nowOpens Prairie View A&M University’s application page

About this role

From Prairie View A&M University’s careers page.

Job Title

Postdoctoral Researcher (Artificial Intelligence)

Agency

Prairie View A&M University

Department

Department Of Computer Science

Proposed Minimum Salary

Commensurate

Job Location

Prairie View, Texas

Job Type

Staff

Job Description

Important Immigration information:

_____________________________________________________________________________

This position is funded by restricted or grant funds. Continued employment is contingent upon the availability and renewal of restricted or grant funding.

Responsibilities:

Develop, train, validate, and optimize machine learning and deep learning models for process microstructure prediction using E-PBF process parameters and time-resolved in-situ BSE data, with particular emphasis on transformer-based and other sequence-learning architectures, in coordination with the PI and project objectives.
Process, clean, curate, align, and analyze high-resolution image and process datasets; develop reproducible pipelines for data quality assessment, feature extraction, labeling, and model-ready dataset generation.
Develop and evaluate methods that connect scan strategies, process parameters, in-situ observations, and resulting microstructural features, including assessment of model performance, uncertainty, repeatability, and generalization, in consultation with the PI.
Contribute to real-time AI-enabled closed-loop control by developing prediction and control approaches that can inform or adapt E-PBF scan strategies based on sensor feedback, in alignment with the PI’s research direction and project goals.
Integrate machine learning components with data-acquisition and control workflows in collaboration with experimental researchers and engineering collaborators.
Maintain clear documentation of datasets, model architecture, software, computational workflows, experiments, and results, and contribute to a well-organized research codebase and repository.
Provide leadership within the research group by taking ownership of project components and coordinating research tasks with collaborators.
Mentor and assist with the supervision of undergraduate and graduate students in machine learning, data analysis, scientific computing, research methods, and technical communication, in coordination with the PI.
Train new group members on computational tools, reproducible machine learning workflows, and research best practices.
Assist the principal investigator with manuscripts, research proposals, technical reports, presentations, and other scholarly outputs.
Performs other duties as assigned.

Required Education and Experience:

Ph.D. in Computer Science, Electrical or Computer Engineering, Mechanical Engineering, Materials Science and Engineering, Data Science, Applied Mathematics, or a closely related discipline.
At least one year of research experience applying machine learning, deep learning, computer vision, scientific data analytics, or related data-driven methods.

Required Knowledge, Skills, and Abilities:

Strong programming skills in Python and experience with machine learning libraries such as PyTorch, TensorFlow, scikit learn, and Keras.
Knowledge of machine learning, deep learning, data analytics, and model evaluation.
Experience with data processing pipelines, statistical analysis, and data visualization tools including matplotlib, seaborn, and Plotly.
Strong written and verbal communication abilities and the ability to collaborate with multidisciplinary teams.
A record of contributing to peer reviewed publications.

Job Posting Close Date:

Until Filled

Required Attachments:

Resume or Curriculum Vitae
Cover Letter

Application Submission Guidelines:

Background Check Requirements:

Equal Opportunity/Veterans/Disability Employer.