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Posted: 23-Apr-26
Location: Ann Arbor, Michigan
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Internal Number: 276657
All applicants must apply for this position through this INTERFOLIO LINK. You will be asked to provide 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 (evaluations or awards, if available). Additionally, you will be asked to provide the name and contact information for two letters of support. Please email cscs.recruiting@umich.edu with any questions.
The Center for the Study of Complex Systems (CSCS) at the University of Michigan is seeking to fill a full-time academic year Lecturer I position. 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.
Example classes are listed below. (Precise course load will be discussed/assigned based on the qualifications of the applicant.)
CMPLXSYS 100 (Complexity: From Simple Rules to Complex Behavior): In this course, we explore a broad range of introductory topics in complex systems, examining how interactions between individuals can lead to emergent patterns, in systems ranging from cells, to societies, to climate change. The course also provides a friendly introduction to programming in an applied context.
CMPLXSYS 251(Computational Social Sciences): 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 (Introduction to Agent-Based Modeling): 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 325 (Memes, Measles, and Misinformation): This course explores how contagious processes can help us understand a range of different phenomena observed in the real world- ranging from infectious disease transmission, to the spread of information, misinformation, and disinformation. We also explore the feedback and interactions between many of these different transmission systems.
CMPLXSYS 391 (Introduction to Modeling Political Processes): 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.
CMPLXSYS 445 (Introduction to Information Theory for the Natural Sciences): This course introduces the basic tools of Information Theory. Entropy, Relative Entropy, and Information, and highlights their utility with applications drawn from various disciplines. After introducing the basics of probability theory and information theory, we explore topics including coding, data compression, channel capacity, thermodynamics, population dynamics, gene transcriptions, network science and more.
This is a single academic year (Fall 2026 and Winter 2027) instructional appointment that may be extended subject to departmental needs and satisfactory performance.
The initial appointment period is for the academic year 2026-27. Responsibilities include teaching undergraduate and/or graduate courses as an instructor of record, and/or discussion or lab section leader, depending on the department's needs. Duties are expected to include teaching, developing course materials, evaluating and grading students, and holding regularly scheduled office hours. A typical full-time (100% effort) load is three courses per semester. Full-time and part-time positions are available.
Candidates should have a Master's degree or Ph.D. in Complex Systems or a related field such as physics, applied mathematics, network science, EEB, or sociology, and some college-level teaching experience.
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.
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.
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.
Please contact cscs.recruiting@umich.edu with any questions.
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.
Review of applications will begin on May 1, 2026, and will continue until the position is filled.
The University of Michigan is an equal employment opportunity employer.
