Cooperative AI Foundation
FINAL WEEK TO APPLY: We’re Hiring an Administration Associate!
You’ll play a vital, behind-the-scenes role in a range of activities—from helping deliver our PhD Fellows programme to managing end-to-end logistics for our events. If you bring strong administrative or operations experience, thrive on bringing order to complexity, take pride in getting the details right, and want your work to matter, we’d love to hear from you.
Location: Remote, with opportunities to meet the team every few months. We’re expecting to appoint someone based in the UK.
Salary: We expect salaries offered to be between £50,000 and £55,000.
Find out more and apply: https://www.cooperativeai.com/job-listing/administration-associate
Niklas Lauffer will talk at the Cooperative AI Foundation’s next ‘Fellows’ Spotlight’. He recently completed his PhD at UC Berkeley and will be joining Google DeepMind as a research scientist.
Title: Robust and Diverse Multi-Agent Learning via Rational Policy Gradient
Speaker: Niklas Lauffer, UC Berkeley
Date: Thursday 25 June 2026 (16.00–17.00 UTC)
About: This talk explores how to make adversarial training work in cooperative multi-agent settings by ensuring agents remain rational, thus preventing self-sabotage—a critical failure mode in these types of settings - while still discovering adversarial examples, improving robustness, and learning diverse policies.
Join the Cooperative AI Foundation’s next ‘Updates in Cooperative AI’ seminar with Fernando Santos (Informatics Institute, University of Amsterdam).
Title: Cooperation Through Reputation in Multi-Agent Systems
Speaker: Fernando Santos (Informatics Institute, University of Amsterdam)
Date: Wednesday 29 July 2026 (16.00–17.00 UTC)
About: Fernando Santos examines how reputation sustains cooperation through indirect reciprocity. He explores evolutionary game theory, reinforcement learning across social groups, and how LLM-based reputation systems influence cooperation dynamics.
https://www.cooperativeai.com/seminars/cooperation-through-reputation-in-multi-agent-systems