How to Apply
All applicants should submit a cover letter, a vita, three representative publications, evidence of teaching excellence, a statement of teaching philosophy and experience, a statement of current and future research plans, contributions to diversity, and three letters of recommendation. All application materials must be submitted electronically to: https://apply.interfolio.com/54067. Please direct enquiries about this position to the chair of our faculty search committee, Dr. Kevyn Collins-Thompson (firstname.lastname@example.org). This is a rolling search and we plan to make offers as qualified candidates are identified and continue until all positions are filled. Consideration of applications will begin immediately.
The School of Information at the University of Michigan (UMSI) seeks tenure-track professors at the assistant, associate or full level in the broad field of data science. Successful candidates are expected to be able to teach in UMSI’s online and/or residential programs related to applied data science, and contribute to our vibrant research culture. UMSI seeks applicants who can contribute to the research, teaching, and service missions of the school and the university. Applicants in some of the areas listed below may also be considered for joint research appointments with the Institute for Social Research. We anticipate making several hires across the following overlapping areas of emphasis:
Social media – We seek candidates who will use analysis of large-scale data to understand how social media interactions affect society and institutions. Areas of focus include (but are not limited to): online harassment; discussion norms and spaces; social support; misinformation and disinformation in social media channels; online violence; online civility; and content moderation.
Computational social science -- We seek candidates with a strong computational background who are specifically interested in applications to social science, broadly defined. Candidates should demonstrate excellence in both computational methods and social science theory. Applications of interest include but are not limited to: social network analysis; crisis informatics; political communication; large-scale social experiments; social science applications to health; and simulations of social systems.
Machine learning and causal inference -- We seek candidates whose research combines machine learning, experiments, and econometric techniques of causal inference in order to design, implement and analyze applications at the intersection of social science and information science. We are open to the specific domain of study. Examples include (but are not limited to): network analysis; health and behavior change; ICT for development; user-generated content and other public goods problems; crowdsourcing, collective intelligence and other information aggregation problems.
Learning analytics – We seek candidates in the field of large-scale learning and behavioral data analytics who will focus on understanding, evaluating, and designing systems designed to support teaching and learning in formal and informal educational settings. Successful candidates will combine insights from the learning, cognitive, and computer sciences approaches to working with data generated by teachers and learners interacting in face-to-face, blended, and online contexts. Candidates with experience in experimental and developmental research to build, validate, and effectively deploy analytic technologies or techniques that make a positive impact on learning and teaching will be well-suited to this position. Examples include (but are not limited to) research on: Massive Open Online Courses (MOOCs); behavioral interventions for learners; visualizations of learners/learning to support teaching; the use and impact of social media and other online tools in learning/teaching; feedback systems for learners; or adaptive/personalized learning and tutoring systems.
Application-inspired data science techniques – We seek candidates who have strong interest and expertise in creating novel computational techniques related to data science methodologies, which include but are not limited to: data mining; machine learning and optimization; information retrieval; natural language processing; network analysis; information visualization; and multimedia analysis. Candidates should also have interest in application areas that include (but are not limited to): Web mining; content analysis; behavioral data analysis; mobile and sensor data; recommender systems; social networks; computational social sciences; data for social good; health informatics; learning analytics; digital humanities; transportation informatics; and crowdsourcing.
Computational humanities – We seek candidates who have strong interest and expertise in creating novel computational techniques related to testing and expanding theories in the humanities, which include but are not limited to: cultural analytics; literary text analysis; natural language processing; historical linguistics; museum informatics; and multimedia analysis. Applicants whose work integrates theory or expertise from one or more humanities fields with machine learning or natural language processing and those with work on multilingual or multimodal data will be particularly well suited for this position.
Ethics of AI and data science – We seek candidates who investigate the ethical dimensions and consequences of artificial intelligence (AI) and/or data science, broadly defined. Candidates should be grounded in a discipline or with core expertise in philosophy, public policy, applied ethics (including information ethics, computer ethics, media ethics, data ethics), science and technology studies, law, or some other perspective centrally related to ethics. Candidate research may be focused on the ethics of big data, data science ethics, digital media ethics, critical data science, data justice, the ethics of algorithmic systems, the ethics of machine learning, the ethics of autonomous or semi-autonomous agents (software, vehicles, robots), or another related topic. Any setting or context for the ethical investigation of AI and/or data science will be considered, and this could include media, information, science, medicine, education, war, labor, transportation, and/or the computer industry.
About UMSI and UM
The mission of the School of Information is to create and share knowledge to help people use information -- with technology -- to build a better world. A successful candidate will be committed to, and will directly contribute to our goal of being the best research and teaching institution for the understanding and design of information and its technologies in service of people and society.
The School is home to vibrant research and teaching programs, with 50 FTE professors, and over 800 students. We offer four degrees: a Ph.D.; a Master of Science in Information; a Master of Health Informatics; and a Bachelor of Science in Information. In the fall of 2019 we expect to launch a new online degree: Master of Applied Data Science.
Founded in 1817, the University of Michigan has a long and distinguished history as one of the first public universities in the nation. It is one of only two public institutions consistently ranked among the nation's top ten universities. The University has one of the largest health care complexes in the world and one of the best library systems in the United States. With more than $1 billion in research expenditures annually, the University has the second largest research expenditure among all universities in the nation. The University has an annual general fund budget of more than $2.1 billion and an endowment valued at more than $10.9 billion. For more information about UMSI or other job opportunities please visit www.si.umich.edu
Each contributing member of the UMSI faculty is expected to have teaching effort equivalent to three residential courses per year. In addition to formal classroom and/or online teaching, faculty are expected to work with students by serving as advisors for independent studies, master’s projects and theses, and doctoral dissertations. Job duties include teaching, research and service. Additional job responsibilities include but are not limited to:
- Conducting scholarly research resulting in publications in peer reviewed journals, book chapters, edited books, books, and conference papers
- Seeking outside funding to support their research
- Providing service to the school, University, and the broader academic community by way of committee work, journal editing, and other various opportunities
- Potential travel to attend or present at conferences and/or research meetings
- Ph.D. in an area such as information, computer science, the humanities, social sciences, or other relevant area
- A strong interest in teaching at the undergraduate and graduate level
- A proven record in teaching and research is desirable
- A strong commitment to teaching, interdisciplinary research, and cultural diversity
- Experience teaching online courses preferred
This position is posted as Assistant Professor/Associate Professor /Professor. The rank of the selected candidate will depend upon candidate qualifications.
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 will be performed in compliance with the Fair Credit Reporting Act.
U-M EEO/AA Statement
The University of Michigan is an equal opportunity/affirmative action employer.