Postdoctoral Fellow, Learning Science & AI in Education

Carnegie Mellon University; False Pass (AK); On-site; USD 60,000 - 75,000; Full time

Benefits offered by this job

Competitive salary and benefits

Tuition benefits at CMU

Comprehensive health insurance

Research environment with leading LM/H

Collaborative, fast-scaling project

Job summary

Carnegie Mellon University’s Human-Computer Interaction Institute hosts the PLUS project, aiming to double middle school math learning through hybrid human-AI tutoring. We invite a technology‑oriented postdoctoral fellow to contribute to research on tutoring effectiveness and AI-assisted support tools, advised by Prof.

Ken Koedinger and Dr. Danielle Thomas, with collaboration across CMU, Stanford, and NTO partners.

Qualifications

• PhD in Cognitive Science, Learning Science, Computer Science, Information Systems, or related fields.
• A record of publications in Cognitive Science, Learning Science, AI in Education, and/or Educational Data Mining (conferences such as AIED, LAK, EDM, etc.).
• Collaborative problem-solving skills to work effectively with state assessment professionals toward innovation in assessment.
• Experience working with discourse analyses, such as audio transcription analysis and multimodal data analysis.
• Data analysis skills with R/Python/or similar programming languages.

Responsibilities

• Collaborative problem-solving toward innovation in tutoring with different stakeholders.
• Design and implement evaluations of innovative programs.
• Data analyses and modeling using Python/R/or similar programming languages.
• Co-author research papers on the results of student learning modeling and studies.
• Attend required meetings and participate in seminars and training workshops.

Skills

Python

R

Data analysis

Multimodal data analysis

Education

PhD in Cognitive Science, Learning Science, Computer Science, Information Systems, or related fields

Job description

Carnegie Mellon University’s Human-Computer Interaction Institute hosts the PLUS project, aiming to double middle school math learning through hybrid human-AI tutoring. We invite a technology‑oriented postdoctoral fellow to contribute to research on tutoring effectiveness and AI-assisted support tools, advised by Prof.

Ken Koedinger and Dr. Danielle Thomas, with collaboration across CMU, Stanford, and NTO partners.