Postdoctoral Research Associate, Adaptive Learning Agents and Computational Cognitive Science

This Postdoctoral Research Associate, Adaptive Learning Agents and Computational Cognitive Science position is with University of Arizona in Tucson, AZ. Design, implement, and train new reinforcement learning agents for game-based environments in which task structure and controllability change. Relevant approaches include meta-reinforcement learning, continual learning, model-based reinforcement learning and world models, and transformer-based sequence models for decision-making. Treat trained agents as scientific models of behavior. Fit and compare them with human data from game-based paradigms examining meta-control, cognitive flexibility, and social learning, interpret the representations and strategies they acquire, and relate them to process-oriented cognitive models such as reinforcement learning and sequential sampling models. Develop methods that connect agent behavior with human behavior at the level of mechanism. Use simulation, recovery analyses, and out-of-sample prediction to determine which mechanisms are identifiable and supported by the data. Formulate decisive comparisons among competing accounts of adaptation and help design or refine games and environments within the lab's game-based adaptive platform. Lead first-author manuscripts aimed at machine learning venues and at cognitive science journals, and contribute to collaborative papers with partner sites. Take scientific responsibility for the lab's agent research line, including theoretical framing, modeling decisions, timelines, code quality, and publication. Work with the lab's engineering team, clinical partners, and neuroscience collaborators to connect agents and cognitive models to adaptive tasks and clinical measures, and to extend this work to EEG and eye tracking alongside collaborators with expertise in these methods. Build and maintain reproducible, documented training and analysis pipelines under version control, and release code with publications. Contribute to grant applications and prepare independent fellowship or career-development proposals with support from the Principal Investigator. Co-mentor graduate and undergraduate researchers on aligned sub-projects, present findings at major national and international conferences, and contribute to shaping the intellectual direction of the lab. Knowledge, Skills, and Abilities Ability to design, implement, and interrogate learning agents, and to reason about why an agent behaves as it does. Knowledge of deep reinforcement learning, meta-learning or in-context learning, model-based reinforcement learning, or sequence models for decision-making, with the ability to apply these methods to understanding human adaptive behavior. Ability to connect computational approaches to questions about human cognition and adaptive behavior. Ability to reason carefully about what a trained model has learned, about alternative explanations, and about what a dataset can and cannot establish. Strong programming skills in Python and a modern deep learning framework such as PyTorch or JAX, including the ability to develop and maintain original research code. Knowledge of model validation methods and reproducible research practices. Ability to set priorities and take ownership of a project from initial formulation through publication. Skill in scientific writing and the ability to effectively communicate rigorous research findings. Ability to collaborate across computational, experimental, engineering, and clinical perspectives, and to give and receive direct feedback. Ability to explain technical choices to colleagues from other backgrounds, and willingness to build shared tools and documentation that the lab can continue to use. Ability to demonstrate intellectual curiosity, reliability, and a collaborative approach to shared research, with a commitment to developing an independent research identity.