Research Associate in Systems and Information Engineering

Postdoctoral research associates sought to develop statistical and algorithmic foundations for reinforcement learning from human feedback (RLHF) and epistemic control of large language models (LLMs). The position is expected to begin on January 1, 2027, with an initial appointment of one year and the possibility of renewal for an additional year, contingent on satisfactory performance and funding availability.

Research Program

The project will study how human preferences and other forms of feedback can be used to train LLMs that reason reliably, recognize uncertainty, and adapt their behavior to the task and the user. Key research areas include:

• Investigating identifiability, sample complexity, generalization, uncertainty quantification, reward misspecification, and the propagation of estimation error from preference models to learned policies.
• Developing adaptive methods for collecting human feedback more efficiently.
• Exploring epistemic control: treating an LLM as a controlled reasoning system rather than a one-shot response generator. This involves formulating choices as a hierarchical decision problem where an epistemic controller selects reasoning actions (e.g., problem decomposition, hypothesis generation, information retrieval, evidence verification, consistency checking, confidence calibration, or deferring judgment).

Possible research outcomes include finite-sample guarantees for preference-based estimators, uncertainty-aware reward modeling, off-policy evaluation methods, adaptive experimental designs, and algorithms for epistemic control under partial observability. Empirical studies may use open-source LLMs and benchmark tasks in reasoning, scientific question answering, code review, tutoring, or decision support.

Responsibilities

• Develop theoretical and computational methods for RLHF, preference learning, and epistemic control.
• Establish statistical or algorithmic guarantees where appropriate.
• Design and implement empirical evaluations using modern machine-learning frameworks and open-source LLMs.
• Prepare research papers for publication in leading machine-learning, artificial-intelligence, statistics, operations research, or systems venues.
• Collaborate with faculty, graduate students, and other project researchers.
• Contribute to the intellectual development of a broader research program on reliable and human-centered AI.

Qualifications

A Ph.D. in electrical engineering, computer science, statistics, applied mathematics, operations research, systems engineering, or a closely related field by the appointment start date.

• Preferred Qualifications:
  • Strong background in theoretical machine learning, statistical learning theory, optimization, or reinforcement learning.
  • Experience establishing theoretical guarantees for deep-learning or other high-dimensional statistical models.
  • Research on efficient training or inference for large neural networks, including mixture-of-experts models, pruning, quantization, or related methods.
  • Experience implementing and evaluating large models using frameworks such as PyTorch, JAX, or TensorFlow.
  • Interest in extending theoretical and computational expertise toward RLHF, human-centered AI, LLM alignment, and reliable reasoning.
  • Experience with large language models, mechanistic interpretability, preference learning, inverse reinforcement learning, human-feedback data, or human-subject experimentation (desirable but not required).

The position is especially well suited for a researcher interested in connecting rigorous machine-learning theory with the development of efficient, transparent, and reliable LLM systems.

Pay

Estimated salary range is $62,000 - $67,000, commensurate with experience.

Benefits

For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits.