Postdoctoral Researcher (PhD) – Human Inspired AI Full-time

Meta is seeking Research Scientists to join its Fundamental AI Research (FAIR) organization, focused on making significant advances in Human inspired AI. This covers the development of benchmarks and tasks that directly compare humans and machines on high level tasks (reasoning, learning, world modeling), and the design of algorithms that learn like humans do (from sparse, unlabelled and noisy data). We publish groundbreaking papers and release frameworks/libraries that are widely used in the open-source community fostering the advancement of AI at its intersection with the study of natural intelligence. We seek researchers with a mixed expertise in machine learning and cognitive, developmental or language science to join our research team to foster cutting-edge research in human inspired AI.

Postdoctoral Researcher (PhD) – Human Inspired AI Responsibilities

• Publishing state-of-the-art research papers in both high impact machine learning and cognitive science outlets.
• Conducting independent research that investigates how AI can improve the science of learning in biological organisms and vice versa.
• Open sourcing high quality code and reproducible results for the community.

Minimum Qualifications

• Currently has, or is in the process of obtaining, a PhD in mathematics, statistics, computer science, cognitive or language science with a strong background in both theoretical and empirical disciplines.
• Deep interest in cross-disciplinary communication towards conducting research in human inspired AI.
• Evidence of research background with publications in top-tier conferences/journals in the related fields.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Preferred Qualifications

• Proven track record of achieving significant results as demonstrated by grants, or fellowships, as well as publications in *ACL, top speech and language conferences and/or top cognitive (neuro)science journals.
• Contributions to affinity workshops at machine learning conferences (e.g. NeurIPS, ICML) and/or cognitive science conferences (ICIS, CogSci, etc) welcome.
• Experience building systems based on machine learning and/or deep learning methods.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience working and communicating cross functionally in a team environment.