Through Orthogonal Research and Education Lab (OREL), and in with guidance and approval from Head Scientist Dr. Bradly Alicea, I offer opportunities to gain research experience and collaborate on topics of interest. Cross-disciplinary research is a major theme, so while there is no expecation to be an expert in all facets, strong interest and some experience in a specific area and willingness to learn in others is highly valued.

The two main teams I supervise are noted below, although if there is a topic of interest you would like supervision or collaboration on elsewhere at OREL, let me know.

Current Openings at OREL

Please see OREL’s Get Involved web page for the most up to date list of open positions within our lab.

Select Opportunities for Involvement

These topics listed below may not have current positions listed within OREL’s listings, but I am generally interested in them so feel free to reach out to me (jesse @ for specific project or collaboration discussion.

Cognition Futures - Internships (Topical Focus)

Inspired from developmental psyhology and cognitive science, and in concert with OREL’s Developmental AI project, we’re looking for a researcher to investigate theories of learning across disciplines, with the aims of fleshing out computational or simulation countparts as available. Specific topics include:

  • Theories of Learning
  • Cybernetics or Control Theory
  • Meta-learning (in machine learning)

Cognition Futures - Research Assitant: Mapping Trajectories in Cogntive Science (Open Track)

This role is more indepedent, less topically driven, but would be useful for someone versed within the general cognitive science / philosophy / computer science space. Which trajectories would you like to map? Contact me to discuss what you’d like to focus on.

Soiety Ethics Technology Team (AI Ethics)

Project-based roles are in development, but reach out if you have a specific interest in:

  • Law keeping pace with technological advancement
  • Shared Risk & Cooperation (upcoming projects in agent-based modeling and multi-agent learning)