Research Associate - Mathematical Sciences

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• Mathematical Sciences | Posting Details Posting Details Posting Number S07016P Position Title Research Associate
• Mathematical Sciences Functional Title Department Mathematical Sciences Salary Range $55,000
• $57,000; depending on qualifications Pay Basis Monthly Position Status Regular full-time Location Richardson Position End Date (if temporary) Posting Open Date 05/27/2026 Posting Close Date Open Until Filled No Desired Start Date 08/01/2026 Job Summary A postdoctoral Research Associate position in statistics is available in the Department of Mathematical Sciences at the University of Texas at Dallas.

Dr. Jiayi Wang is seeking highly motivated candidate with expertise in statistical learning and machine learning, both theoretical andputational. Minimum Education and Experience Ph.D in a related field. Candidates who have successfullypleted all requirements for the Ph.D. but whose degree has not yet been formally conferred may be eligible to begin employment provided they submit an official letter from the Registrar or Graduate School of the degree-granting institution certifying that all doctoral requirements have been satisfied and specifying the expected date of conferral. Appointment and continued employment are contingent upon formal conferral of the Ph.D. Preferred Education and Experience PhD in related fields. Other Qualifications To the extent this position requires the holder to research, work on, or have access to critical infrastructure as defined in Section 117.001(2) of the Texas Business and Commerce Code, the ability to maintain the security or integrity of the critical infrastructure is a minimum qualification to be hired and to continue to be employed in the position. Essential Duties and Responsibilities The selected candidate is expected to conduct research in the following areas: Reinforcement Learning (RL) in

Complex Environments:

Offline and online RL with high-dimensional, unstructured variables, partially observed states, and hidden actions; New methodological and algorithmic developments for Continuous-time RL.

Missing Data Problems:

Novel imputation and inference techniques in various missing data problems. Statistical Inference with

Modern Generative Models:

Theory and methodology for diffusion models and other deep generative models; Uncertainty quantification, robustness, and efficiency in generative modeling; Applications in scientific and industrial domains. Physical Demands and Working Conditions Physical Activities Working Conditions Additional Information Work is conducted onsite and in-person. What We Can Offer UT Dallas is an Equal Opportunity Employer, we offer an employee-friendly work environment with aprehensive benefits package including: Competitive Salary Tuition Benefits Internal Training Medical insurance

• including 100% paid employee medical coverage for full-time employees Dental Insurance Vision Insurance Long and short-term disability Retirement Plan Options Paid time off Paid Holidays All UT Dallas employees have access to various professional development opportunities, including a membership to Academic Impressions, Learning, and UT Dallas Bright Leaders Program.

Visit hr.utdallas.edu/empoloyees/benefits/ for more information. Special Instructions Summary Important Message 1) All employees serve as a representative of the University and are expected to display respect, civility, professional courtesy, consideration of others and discretion in all interactions with members of the UT Dallasmunity and the general public. 2) The University of Texas at Dallas ismitted to providing an educational, living, and working environment that is weing, respectful, and inclusive of all members of the universitymunity. UT Dallas does not discriminate on the basis of race, color, religion, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, national origin, disability, genetic information, or veteran status in its services, programs, activities, employment, and education, including in admission and enrollment.