University of Michigan

What you'll study

The PhD emphasises original research at the intersection of people, information, and technology. Students undertake advanced coursework in areas such as research design and methods, statistics for social and data science, machine learning and data mining, human–computer interaction, information retrieval, social computing and information policy. Core training typically covers qualitative and quantitative research methods, ethics and responsible research practices, and domain-specific seminars aligned with each student’s research programme.

Programme structure combines coursework, research practica and teaching apprenticeship with focused dissertation research. Early years are spent building foundational knowledge and completing required methods and theory seminars; students also participate in lab groups, topical reading seminars and practicum projects. After completing preliminary examinations or a qualifying milestone, students concentrate on dissertation proposal development and original research under the supervision of one or more faculty advisors.

• Advanced research methods (qualitative, quantitative, computational)
• Statistics and data analysis for information research
• Human–computer interaction and user-centred design
• Machine learning, information retrieval and data mining
• Social computing, social networks and digital media studies
• Information policy, ethics and governance
• Teaching and research practicum

Entry requirements

Successful applicants normally hold a strong bachelor's or master's degree in a relevant field (for example information science, computer science, statistics, social science, cognitive science, or a related discipline). Evidence of prior research potential is essential: this can include publications, a research master’s thesis, project reports or substantial research experience.

Applications usually require a statement of purpose outlining research interests and fit with faculty, a CV, academic transcripts, and at least three academic or professional letters of recommendation. Many applicants also submit writing samples or research papers. GRE requirements vary by unit and may be optional; international applicants must demonstrate English language proficiency through recognised tests unless exempted by institutional policy.

Successful candidates typically demonstrate:

• Strong academic record and quantitative preparation when relevant to proposed research
• Clear research interests that align with faculty expertise
• Evidence of independent research ability or potential
• Good fit with the interdisciplinary environment of the school

Career prospects

Graduates of the PhD in Information Science pursue a wide range of careers. Many secure tenure-track positions in universities and colleges, while others join industry research labs, technology companies and product teams as research scientists, senior user experience researchers, data scientists or research engineers. Graduates also move into leadership roles in public policy, think tanks, government research organisations and non-profit research centres where they contribute to technology policy, digital governance and information ethics.

Typical roles include:

• Assistant professor or researcher in information science, computer science, communication or related departments
• Research scientist or research engineer in corporate R&D labs
• Senior UX/behavioural researcher or product research lead
• Data scientist or machine learning researcher with a focus on socially situated data
• Policy analyst or researcher specialising in information policy, privacy or algorithmic accountability

Why study at University of Michigan

The University of Michigan offers an interdisciplinary environment with a strong School of Information and close ties to computer science, social science and engineering departments. Students can collaborate with faculty across campus and access multi-disciplinary research centres and institutes that support large-scale, cross-cutting projects. The university provides substantial research infrastructure, including computational resources, specialised libraries and data archives, as well as opportunities for industry and government partnerships.

Doctoral students benefit from active seminar series, lab groups and grant-funded research projects, mentoring from internationally recognised faculty, and structured professional development for teaching and research careers. The campus ecosystem and alumni network also support diverse career pathways in academia, industry research and public-interest work.