Postdoc available in our interdisciplinary Moral AI group! Description: We have an opening for a joint two-year postdoctoral position at the School of Computer Science at Carnegie Mellon University and the Social Science Research Institute, Duke Institute for Brain Sciences, and Kenan Institute for Ethics at Duke University (with the possibility of renewal for a third year). The postdoc can be employed through any of these departments in either University, depending on their preference. The candidate is expected to live in either Pittsburgh or Durham depending on their chosen home department, but will be asked to travel occasionally to the other location.
The focus of the position is on conducting impactful research on moral artificial intelligence, addressing problems such as: Designing artificial intelligence systems to model and predict human moral judgments Designing effective elicitation mechanisms and interfaces to capture moral preferences Developing techniques for implementing and evaluating AI systems that are based on these modeled preferences, and assessing the dynamic impact of these systems on the preferences and behavior of users who interact with them Qualifications: Applicants should hold a Ph.D. in computer science, statistics, mathematics, computational neuroscience, computational cognitive science, or related technical fields and have expertise in Machine Learning and AI / statistics / data science. Additional background in moral psychology, moral philosophy, (AI or other) ethics, HCI, social decision-making and/or social science is preferred, but not required. Interested applicants should apply online <https://apply.interfolio.com/113237> <https://apply.interfolio.com/113237> (https://apply.interfolio.com/113237) or contact Dr. Jana Schaich Borg via email ([email protected]), including a CV and statement of research interests, and the name of three referees that can provide letters of recommendation. We are strongly committed to a diverse academic environment and place high priority on attracting underrepresented candidates.
