University of Tennessee
#607
QS World University Rankings 2026
28
QS 2026 overall score
Ranking data
QS World University Rankings source#607
QS World University Rankings 2026
#481
QS World University Rankings 2025
28
QS 2026 overall score
Indicator-level data
Each card keeps the QS 2026 score and rank separate. A missing value is not estimated.
Academic reputation
- QS 2026 score
- 14.3
- QS 2026 rank
- #701
Employer reputation
- QS 2026 score
- 11.7
- QS 2026 rank
- #701
Faculty-student ratio
- QS 2026 score
- 12.9
- QS 2026 rank
- #801
Citations per faculty
- QS 2026 score
- 69.3
- QS 2026 rank
- #220
International faculty ratio
- QS 2026 score
- 6.9
- QS 2026 rank
- #801
International student ratio
- QS 2026 score
- 4.6
- QS 2026 rank
- #801
International student diversity
- QS 2026 score
- 9.8
- QS 2026 rank
- #801
International research network
- QS 2026 score
- 68.6
- QS 2026 rank
- #557
Employment outcomes
- QS 2026 score
- 25.2
- QS 2026 rank
- #636
Sustainability
- QS 2026 score
- 54.8
- QS 2026 rank
- #606
About University of Tennessee
University of Tennessee connects five public research strengths to different forms of inquiry
The University of Tennessee research office presents advanced materials and manufacturing, artificial intelligence, energy and environment, future mobility, and health and wellness as visible strengths. Those labels are useful starting points because they point toward genuinely different questions. A materials question can involve physical composition, processing, testing, durability, or manufacturing conditions. An artificial-intelligence question may concern a model, a dataset, a decision process, an interface, or the consequences of system use. Energy and environmental work can require measurements, field observations, maps, laboratory analysis, policy records, or system models. Health and wellness questions may instead rely on biological evidence, service practices, population information, or lived experience.
The university home page also makes hands-on research and service-learning visible within its wider academic setting. That connection is worth reading carefully. Hands-on activity can mean laboratory work, field observation, design, prototyping, data analysis, or work with a community partner, depending on the subject. Service-learning can involve a public problem, an organisation, a place, or a population, but it does not identify a method by itself. A useful next source should therefore show what is being examined and how. Moving from a broad strength to a named project, laboratory, researcher, or public output keeps a topic search tied to observable work rather than to an appealing label.
Tennessee's place-based innovation work makes the setting part of the research question
The research office describes Cherokee Farm as a setting where multidisciplinary research and development activities are co-located with partner activity. This makes place relevant to the inquiry. A question about mobility, for example, may concern a vehicle, a route, an energy source, a control system, or a public decision. Each version changes the setting and the evidence needed. A vehicle question may use components, sensor traces, test results, and design records. A route question can use maps, counts, observations, and travel data. A policy question may require documents, interviews, administrative records, and local context. The same word can lead to several different investigations.
The public research page also distinguishes core facilities, department laboratories, advanced computing, and co-location spaces. These are not interchangeable descriptions. A facility page can identify an instrument or technical environment. A laboratory page may identify a group, a discipline, or a research process. Advanced-computing material may clarify a computational setting, while a co-location page can describe how organisations work near one another. None of these sources alone confirms that a particular topic is active or that a particular method belongs to it. Their value is practical: they help a reader identify which kind of evidence should be checked next.
A Tennessee search becomes clearer when it states the focus, place, and record
A focused University of Tennessee inquiry starts by naming a concrete object. It might be a manufactured component, an artificial-intelligence system, an energy process, a mobility decision, a health practice, or an environmental condition. Then name the setting in which it appears: a laboratory, a computing environment, a field location, a transport network, a clinic, a workplace, or a community organisation. Finally, identify the material that could support an answer, such as samples, readings, source code, models, images, documents, interviews, surveys, test results, or observations. This sequence turns the university's public strengths into a search tool rather than a general statement about fit.
The available official pages establish broad strengths, research-support settings, and a public emphasis on collaboration. They do not show that every unit studies every topic within those strengths, and they do not settle the suitability of a proposed approach. A careful profile should retain that boundary. When a local page describes the same object, setting, and evidence, it can support a stronger connection. When the material only shares a broad term such as energy or health, it remains a lead for further checking. That distinction lets the University of Tennessee page support serious exploration without extending the official evidence beyond what it says.
Institution record
- Country
- United States of America
- Region
- Americas
- Status
- Public
- QS size code
- XL
- Profile record updated
- July 24, 2026
This date shows when this profile was refreshed. It is not a source-verification date from QS or the university.
Search opportunitiesOpportunity records may use a different form of the institution's name. Confirm every listing with its original source.