Human Computer Interaction Research Assistant Jobs

Description
The Human-Computer Interaction Institute (HCII) at Carnegie Mellon University (CMU) invites applications for a two-year Postdoctoral Fellowship focused on advancing human-centered AI in training environments that support well-being, customer service, and interpersonal skills. The position offers a unique opportunity to conduct cutting-edge, interdisciplinary research at the intersection of psychology, education, organizational behavior, and artificial intelligence. The goal is to integrate expertise in mental health, customer service, digital therapeutics, and AI to create impactful, scalable solutions for training and support in real-world settings.
Robert Kraut, Haiyi Zhu, Sherry Wu, and Yi-Chia Wang in the Human-Computer Interaction Institute at CMU and Diyi Yang in the computer science department at Stanford University are available as mentors. Initial positions will be full-time in Pittsburgh, PA. CMU offers a vibrant interdisciplinary research environment across the Human-Computer Interaction Institute, Language Technologies Institute, and School of Computer Science, with extensive opportunities for collaboration, mentorship, and professional development.
Project Overview:
Our projects try to understand the needs for, develop deployable systems, and study the impact of AI-powered training environments designed to enhance psychosocial skills in real-world settings:
- Mental health and well-being: Collaborating with large-scale online support platforms (e.g., 7Cups), we are developing prototypes that use LLM and conversational agents to deliver scalable, experiential micro-skills training (such as empathy, active listening, and feedback) for mental health providers. Our goal is to improve access, efficacy, and personalization in mental health and support interventions.
- Customer support and workplace training: Partnering with industry leaders (e.g., Teleperformance), we design simulation-based conversational training for customer service representatives and their supervisors. These environments help train staff in complex social scenarios, stress management, and de-escalation skills, using realistic virtual agents and personalized feedback.
The postdoctoral researcher will use a user-centered, iterative design approach, co-developing with frontline practitioners and conducting robust evaluations, such as mixed-method analyses and field trials, to assess the impact of end-to-end prototypes on skills acquisition, workplace outcomes, and client wellbeing.
Qualifications
Required Qualifications:
Applicants should have:
• Expertise in a relevant research field, such as health/clinical/counseling psychology, human communication research, human-computer interaction, social psychology, organizational behavior, or education.
• Track record of empirical research with practitioners or end-users in relevant contexts.
• Strong research record, with publications in leading conferences or journals.
Preferred Qualifications:
We are especially excited about candidates who have:
• Interest in or experience with AI/NLP-powered training systems.
• Practical experience in a relevant application domain, including mental health/therapy, well-being interventions, education/coaching, or customer support training.
• Experience designing, implementing and evaluating technology-based training.
• Experience designing and running random-assignment experiments or clinical trials
• Familiarity with user-centered, participatory co-design or mixed-methods research.
• Experience collaborating with platform or industry partners, or in translating research to practice.
• Passion for interdisciplinary work and translational research bridging domain expertise and digital innovation.
Application Instructions
Applicants should submit the following:
• A CV
• At least two letters of reference
• A 2-page research statement describing your interests and domain expertise and their fit with the aims of our AI-driven training research
Applications will be reviewed on a rolling basis until the position is filled.