OPEN POSITIONS [Deadline: January 15, 2024]
We are currently looking for two highly self-motivated Ph.D. students who are interested in research focused on (1) Large-Scale Optimization or (2) Artificial Intelligence/Machine Learning/Deep Learning to join our SSRS Lab in the Department of Industrial and Systems Engineering at the University of Missouri.
These positions are available as early as the Fall of 2024. The positions will be funded by a combination of research and teaching assistantships, which include full tuition support, stipend, and benefits. The 12-month assistantship is available with a possibility of renewal based on satisfactory performance.
Successful candidates should meet the following qualifications:
B.S. or M.S. in Industrial and Systems Engineering, Operations Research, Transportation Engineering, Statistics, Computer Science/Engineering, Mathematics, or related fields,
Experience in writing or presenting academic research papers for submission to peer-reviewed journals/conferences, or an M.S. thesis,
Strong programming skills (Python, CPLEX, GAMS, AMPL, R, MATLAB, etc.),
Prior research experience in Mathematical Optimization or Machine Learning.
If you are interested, please email a cover letter, a resume or CV, unofficial transcripts, GRE scores (if any), TOEFL/IELTS scores (if applicable), and writing samples (e.g., journal/conference papers) to Dr. Hyeong Suk Na (email@example.com). Candidates must also apply to the University of Missouri to be considered prospective members of the SSRS Lab. Review of applicants will begin immediately and continue until the positions are filled.
Graduate Education at Mizzou
Graduate Admissions | Ph.D. in Industrial Engineering | M.S. in Industrial Engineering
Those from traditionally underrepresented groups in STEM are particularly encouraged to get in touch with Dr. Hyeong Suk Na. The University of Missouri is an affirmative action/equal opportunity employer and does not discriminate on the bases of age, color, disability, gender, gender identity, genetic information, marital status, national or ethnic origin, race, religion, sexual orientation, or veteran status.