Post-Doctoral Associate

Carnegie Mellon University; Pittsburgh (Allegheny County); On-site; USD 60,000 - 90,000; Full time

Job summary

An established industry player in computational social science is seeking two Post-Doctoral Associates to explore online harms through innovative research. The role involves applying agent-based modeling and social network analysis to assess influence campaigns, meme analysis, and online threat detection. Ideal candidates will be proficient in C, C++, and Python, and have a passion for studying behavior across cultures. Join a dynamic team at a leading academic institution where you will contribute to groundbreaking projects and share your findings at prestigious conferences. This is a unique opportunity to make a significant impact in the field of social science.

Qualifications

• PhD in a STEM field, including computational or mathematical social science.
• Proficiency in programming with C and Python.
• Experience in social network analysis and agent-based models.

Responsibilities

• Develop virtual experiments and analyze social media data.
• Co-author papers and present findings at conferences.
• Work on agent-based simulation and empirical analysis projects.

Skills

C Programming

C++ Programming

Python Programming

Agent-Based Modeling

Social Network Analysis

Statistics

Education

PhD in STEM

Tools

Network Analysis Techniques

Geo-Spatial Analysis

Image Assessment

Job description

Carnegie Mellon University: School of Computer Science: Software and Societal Systems

Location: Pittsburgh, PA

Open Date: Aug 14, 2024

Description

The Software and Societal Systems Department, in the School of Computer Science at Carnegie Mellon University, is looking to hire two Post-Doctoral Associates interested in applying computational social science approaches to the study of online harms. Ongoing projects include assessing influence campaigns and their impact, meme analysis, online threat detection, techniques for countering information maneuvers, and examining differences in online harms across countries and geo-spatial regions. Successful applicants should have experience in agent-based modeling and social network analysis.

The ideal candidates should be proficient in programming in C or C++, with additional skills in Python, and familiar with network analysis techniques such as community detection and centrality metrics. Experience with geo-spatial analysis, image assessment, meme analysis, and large-scale data involving millions of actors and messages is preferred. Candidates will work on related agent-based simulation and empirical analysis projects, develop virtual experiments, analyze social media data, document results, co-author papers with Dr. Carley and PhD students, and present findings at conferences such as IDeaS. An interest in studying behavior in non-English languages like Chinese and Russian is also desirable.

Qualifications

• PhD in a STEM field, including computational or mathematical social science
• Proficiency in programming with C and Python
• Basic knowledge of statistics
• Experience in social network analysis
• Experience with agent-based models: building, validating, and working with ABMs
• Excellent academic writing skills