Posting Number: req27365
Department: Psychology
Location: Tucson Campus
Address: Tucson, AZ USA
Position Highlights
The NAIADEL Lab in the University of Arizona Department of Psychology seeks a Postdoctoral Research Associate with expertise in developing and analyzing adaptive learning agents to investigate the computational mechanisms of human intelligence and agency. The lab builds a neurocomputational theory of adaptive intelligence and agency. We study how people and artificial agents infer the structure of their environment, judge what they can control, revise those judgments after setbacks, and change their strategies when conditions change.
Computational cognitive models and modern deep learning are our main instruments for this work. We treat deep reinforcement learning agents and sequence models as scientific models of human behavior. We train them, fit them to human data, interpret what they have learned, and compare them with process-oriented cognitive models such as sequential sampling models and reinforcement learning models. These approaches offer complementary accounts of adaptation. Meta-learning agents can model how learning strategies change with experience. Learned world models can represent environmental dynamics and support planning. Sequential sampling models characterize the dynamics of individual decisions. We ask when these accounts explain the same behavior, where they diverge, and how they can be integrated into a theory of adaptive intelligence.
The candidate will lead the development of new adaptive agents for the lab's game-based environment, in which task structure and controllability change across game missions and over time. They will train these agents on the same games that people play, compare agent and human behavior on data from more than 1,000 participants across online, in-person, and clinically characterized samples, and use the results to test and refine the lab's theory of agency. The work will generate first-author papers and an agent-based research line the candidate can carry into an independent program.
Recorded human data and a Gymnasium-compatible runtime for the lab's games allow modeling to begin in the first weeks. Work with engineers and game designers allows new agents and new environments to be tested quickly. Resources include HPC clusters, GPU/CPU nodes, cloud compute, EEG, fMRI, and eye-tracking.
The lab is building a shared computational core of models, environments, and training pipelines on which its research runs, and the postdoc will substantially shape its design. They will join an active community of computational cognitive modelers and computer scientists across campus.
International collaborations connect the candidate to machine learning, neuroscience, and clinical partners and to a global scientific network. The position offers close mentorship, funded international conference travel, dedicated compute allocations, fellowship support, and planning for independence, authorship, and project portability.
The initial appointment is for one year, renewable based on satisfactory progress. The start date is negotiable; applications are reviewed on a rolling basis.
Visa sponsorship may be available for this position.
Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more!
The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocation services, please click here.
Duties & Responsibilities
Department: Psychology
Location: Tucson Campus
Address: Tucson, AZ USA
Position Highlights
The NAIADEL Lab in the University of Arizona Department of Psychology seeks a Postdoctoral Research Associate with expertise in developing and analyzing adaptive learning agents to investigate the computational mechanisms of human intelligence and agency. The lab builds a neurocomputational theory of adaptive intelligence and agency. We study how people and artificial agents infer the structure of their environment, judge what they can control, revise those judgments after setbacks, and change their strategies when conditions change.
Computational cognitive models and modern deep learning are our main instruments for this work. We treat deep reinforcement learning agents and sequence models as scientific models of human behavior. We train them, fit them to human data, interpret what they have learned, and compare them with process-oriented cognitive models such as sequential sampling models and reinforcement learning models. These approaches offer complementary accounts of adaptation. Meta-learning agents can model how learning strategies change with experience. Learned world models can represent environmental dynamics and support planning. Sequential sampling models characterize the dynamics of individual decisions. We ask when these accounts explain the same behavior, where they diverge, and how they can be integrated into a theory of adaptive intelligence.
The candidate will lead the development of new adaptive agents for the lab's game-based environment, in which task structure and controllability change across game missions and over time. They will train these agents on the same games that people play, compare agent and human behavior on data from more than 1,000 participants across online, in-person, and clinically characterized samples, and use the results to test and refine the lab's theory of agency. The work will generate first-author papers and an agent-based research line the candidate can carry into an independent program.
Recorded human data and a Gymnasium-compatible runtime for the lab's games allow modeling to begin in the first weeks. Work with engineers and game designers allows new agents and new environments to be tested quickly. Resources include HPC clusters, GPU/CPU nodes, cloud compute, EEG, fMRI, and eye-tracking.
The lab is building a shared computational core of models, environments, and training pipelines on which its research runs, and the postdoc will substantially shape its design. They will join an active community of computational cognitive modelers and computer scientists across campus.
International collaborations connect the candidate to machine learning, neuroscience, and clinical partners and to a global scientific network. The position offers close mentorship, funded international conference travel, dedicated compute allocations, fellowship support, and planning for independence, authorship, and project portability.
The initial appointment is for one year, renewable based on satisfactory progress. The start date is negotiable; applications are reviewed on a rolling basis.
Visa sponsorship may be available for this position.
Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more!
The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocation services, please click here.
Duties & Responsibilities
- 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.