How to Apply
All applicants must apply to this posting through this website, careers.umich.edu. In addition, please email [email protected] with the following information: a cover letter discussing your interest in and fit for the position, curriculum vitae, a statement of teaching philosophy and experience, and evidence of teaching excellence.
Job Summary
The Center for the Study of Complex Systems (CSCS) at the University of Michigan is seeking to fill two 33.33% Lecturer I positions. CSCS is a broad, interdisciplinary unit whose faculty use and develop tools from applied mathematics, computation, physics, statistics, engineering, and network theory to understand questions in the social, biological, and physical sciences.
This is a one-term instructional appointment that may be extended subject to departmental needs and satisfactory performance.
Mission Statement
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Responsibilities*
The initial appointment period is for the winter term of academic year 2025-26. Responsibilities include teaching undergraduate lab or discussion sections and/or undergraduate courses and/or graduate courses as an instructor of record, depending on the department's needs.
CMPLXSYS 251:
Due to the growth in electronic sources such as cell phones, Facebook, Twitter, and other online platforms, researchers now have enormous amounts of data about every aspect of our lives from what we buy, to where we go, to who we know, to what we believe. This has led to a revolution in social science, as we are able to measure human behavior with precision largely thought impossible just a decade ago. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides a hands-on, non-technical introduction to the methods and ideas of Computational Social Science. We will discuss how new online data sources and the methods that are being used to analyze them can shed new light on old social science questions, and also ask brand new questions. We will also explore some of the ethical and privacy challenges of living in a world where big data and algorithmic decision-making have become more commonplace. Each week, students will have the opportunity to try their hand at analyzing big data from sources ranging from online dating profiles to New York City taxicabs to #metoo Tweets and other sources. Note that this course is a 4-credit course that includes a weekly, 2-hour lab component in addition to lecture and discussion.
CMPLXSYS 270:
Many systems can be modeled as being composed of agents interacting with one another and their environment. Agent based modeling (ABM) can be used to explain phenomena in the biological and social sciences that are driven by multi-agent interactions, ranging from evolution, to epidemic spread, to flocking, to cooperation, to racial segregation in neighborhoods. Agent based modeling allows us to explore how simple rules governing agent behavior can lead to remarkably complex emergent phenomena. In this course students will use Python to explore and modify well-studied agent based models of complex systems, as well as formulate models of their own.
CMPLXSYS 391: This class provides an introduction to modeling people and social systems. We learn to construct, manipulate, and evaluate models of people who vote, work, commit crimes, and attend classes. We cover concepts and ideas from game theory, learning theory, complexity theory, and even biology and physics (at a metaphorical level of course.) Though the topics and techniques covered are wide ranging we analyze among other things the wisdom of crowds, the spread of ideas, the causes of racial segregation, and the emergence of riots, they aggregate into a deep methodological coherence. The kind of understanding you won't get by reading the newspaper. By the end, students will understand the strengths and uses of various modeling approaches used in the social sciences and be able to use them. This is not a mathematics course, but it does require a willingness to think abstractly, to carefully contemplate lots of charts and figures, and to do a little algebra. And above all, a commitment to never reading the newspaper in class.
Required Qualifications*
Candidates should have a Master's degree or Ph.D. in Complex Systems or a related field, and some college-level teaching experience.
Additional Information
As one of the world's great liberal arts colleges, LSA pushes the boundaries of what is understood about the human experience and the natural world, and we foster the next generation of rigorous and empathetic thinkers, creators, and contributors to the state of Michigan, the nation, and the world.
To learn more about LSA's Mission, Vision and Values, please visit lsa.umich.edu/strategicvision.
Union Affiliation
This position is covered under the collective bargaining agreement between the U-M and the Lecturers Employee Organization, AFL-CIO, which contains and settles all matters with respect to wages, benefits, hours and other terms and conditions of employment.
Background Screening
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.
Contact Information
Please contact [email protected] with any questions.
Application Deadline
Review of applications will begin immediately and continue until the position is filled. Deadline for applications is 11/29/25. The anticipated date by which an offer will be made is no later than 12/2/25. The appointment opportunity described in this posting is subject to final approval by the academic unit.
U-M EEO Statement
The University of Michigan is an equal employment opportunity employer.