Fully Funded PhD in Industrial Data Analytics: Oklahoma State University (OSU) is one institution seeking a single highly motivated PhD student who will receive a fully funded doctoral position starting in Fall 2026 with the School of Industrial Engineering and Management.
The chosen student will be part of the research team led by Dr Akash Deep and be involved in the development of data-driven approaches to smart and connected systems. This is the job of a lifetime because the applicant would enjoy working at the crossroads of industrial engineering, machine learning, optimization, and intelligent decision systems.
Why Pursue a PhD in Industrial Data Analytics at OSU?
Full Financial Support: The PhD position is fully funded, as it includes a stipend and tuition. Students are able to dedicate all their attention to the development of research, publication, and professional development.
Research on High Impact Topics: The study addresses a couple of emerging fields, such as surrogate modeling, machine learning, Bayesian optimization, active learning, and meta learning.
NSF Project Participation: The student will be engaged in an active NSF-funded project, which would allow the student to get experience in competitive research, large-scale models, and scientific dissemination.
Research Collaborative and Mentorship: In a department, students in this program conduct research directly with Dr. Akash Deep in an intensive research collaborative/mentorship.
Well-known University: The Industrial Engineering and Management program at OSU has a long history of success in research in the area of operations, analytics, and engineering systems.
Position Description – Fully Funded PhD in Industrial Data Analytics
The chosen student will receive a PhD in Industrial Engineering and focus the research on smart and connected systems, data-driven approaches.
The student role encompasses (but is not limited to) the followingl:
- Surrogate Modeling: Innovation of computing models to have a computationally efficient model that can approximate a complex system, so that faster analysis and optimization can be done.
- Machine Learning: The design of predictive systems based on system behavior, pattern recognition, decision support, and real-time adaptation.
- Bayesian Optimization and Active Learning: Designing the system improvement strategies under uncertainty that balance between exploration and exploitation.
- Meta Learning: Creating systems which enable algorithms to learn to learn, to be more adaptable to fluctuating environments and data.
Qualification Requirement – Fully Funded PhD in Industrial Data Analytics
- Good mathematical and analytical abilities and motivation to solve difficult research problems in engineering systems, AI, and optimization should be brought by the applicants to the program. The overall requirements are as follows:
- BS or MS degree in Industrial Engineering, Statistics, Operations Research, Computer Science, Mathematics, or another similar area.
- Good training in graduate-level mathematics and statistics.
- Firm background in probability, statistics, stochastic modeling, and optimization.
- Skilled in one of the programming languages (Python, R, or MATLAB is desirable)
- Good oral and written communication skills.
- Compliance with the general graduate school enrollment requirements of Meet OSU.
How to Apply For a PhD in Industrial Data Analytics at Oklahoma State University
Applicants who are interested and meet the requirements are expected to prepare the following materials:
- A detailed CV
- A cover letter with a description of the previous and ongoing research experience, interests, and objectives in research.
- Publications or research outputs (Optional) such as preprints, accepted manuscripts, or white papers.
All the application documents must be sent via email directly to Dr. Akash Deep via email
Upon review, shortlisted applicants will be approached to have a virtual interview.
Why This PhD Opportunity Is Unique
The successful candidate will:
- Modern tools work on intelligent decision-making across complex systems.
- Have experience with research funded by NSF.
- Understand how to create models to help make engineering decisions in dynamic settings of uncertainty.
- Establish a body of publications by quality journals and conferences.
- Enroll in a program with good networking with industry and academic contacts.
Contact information and Deadline
Application Deadline: Application for Fall 2026 is currently ongoing. Early application is advised!
Massage Dr. Deep via email, or visit his official webpage for more information about the research group and current works.