Research Associate in Systems and Information Engineering

UVA Health•Charlottesville, VA

7d•$62,000 - $67,000•Onsite

About The Position

The Department of Systems and Information Engineering at the University of Virginia seeks postdoctoral research associates to develop statistical and algorithmic foundations for reinforcement learning from human feedback (RLHF) and epistemic control of large language models (LLMs). 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. The position is expected to begin on January 1, 2027. The initial appointment will be for one year, with the possibility of renewal for an additional year, contingent on satisfactory performance and the availability of funding. Research Program Although RLHF and related methods, including direct preference optimization, are now widely used, their statistical properties remain only partially understood. Human feedback is noisy, heterogeneous, context dependent, and shaped by the process through which data are collected. The postdoctoral researcher will investigate questions involving identifiability, sample complexity, generalization, uncertainty quantification, reward misspecification, and the propagation of estimation error from preference models to learned policies. The work may also develop adaptive methods for collecting human feedback more efficiently. A complementary research direction concerns epistemic control: treating an LLM as a controlled reasoning system rather than as a one-shot response generator. A high-level controller may direct the model to decompose a problem, generate alternative hypotheses, retrieve information, verify evidence, check consistency, request clarification, calibrate confidence, or defer judgment. The project will formulate these choices as a hierarchical decision problem in which an epistemic controller selects reasoning actions that the LLM executes through language generation, structured reasoning, or tool use. The postdoctoral researcher will contribute to theory, algorithms, and empirical evaluation. 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.

Requirements

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.

Nice To Haves

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, inverse reinforcement learning, human-feedback data, or human-subject experimentation is 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.

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