Skip to main content
AcademicWingsSTEM discovery
Institution profile

Philipps-Universität Marburg

GermanyEurope

#771

QS World University Rankings 2026

Not listed

QS 2026 overall score

QS World University Rankings data

Ranking data

QS World University Rankings source

#771

QS World University Rankings 2026

#801

QS World University Rankings 2025

Not listed

QS 2026 overall score

QS 2026 indicators

Indicator-level data

Each card keeps the QS 2026 score and rank separate. A missing value is not estimated.

Academic reputation

QS 2026 score
16.3
QS 2026 rank
#701

Employer reputation

QS 2026 score
15.1
QS 2026 rank
#701

Faculty-student ratio

QS 2026 score
7.4
QS 2026 rank
#801

Citations per faculty

QS 2026 score
39.6
QS 2026 rank
#475

International faculty ratio

QS 2026 score
19
QS 2026 rank
#727

International student ratio

QS 2026 score
31.9
QS 2026 rank
#568

International student diversity

QS 2026 score
23.9
QS 2026 rank
#705

International research network

QS 2026 score
65.9
QS 2026 rank
#612

Employment outcomes

QS 2026 score
8.9
QS 2026 rank
#801

Sustainability

QS 2026 score
47.6
QS 2026 rank
#770
University profile

About Philipps-Universität Marburg

Marburg links research centres, technology platforms, and joint projects across different questions

Philipps-Universität Marburg presents profile areas alongside technology platforms, major instrumentation, centres for collaborative research, research units, and priority programmes. These routes are useful because they distinguish a broad field from the people, tools, and organised work behind it. A life-sciences question may involve an enzyme, cell, organism, disease process, image, or instrument. An environmental question may involve a forest, cloud pattern, species, temperature, or local community. A computing question may centre on a model, data set, algorithm, or human decision. Each needs a different route through the university.

Marburg's research activity also connects early-stage researchers, joint projects, and international exchange. That does not make every question international or collaborative by default. It means a reader can look for the form of work that matches the problem. A question about a cloud forest may need field observations and ecological context. A question about a biological molecule may need laboratory techniques and structural analysis. A question about artificial intelligence may need a defined task, data, model behaviour, and consequences for people using the system. The question should guide the institutional route, not the other way around.

Cloud forests, enzymes, artificial intelligence, and cancer research give Marburg distinct objects

Recent Marburg research examples include a unit studying the future of the Galápagos cloud forests, work decoding one of nature's largest enzymes, an artificial-intelligence joint lab, a Microcosm Earth Centre group, cancer research in the fourth dimension, and a European vaccines hub. These examples do not point to one generic scientific method. A cloud-forest question can involve climate, vegetation, altitude, water, species, and change over time. An enzyme question can involve molecular structure, biological function, and laboratory analysis. An artificial-intelligence question must identify the task, data, model, and setting in which a system is used.

Cancer research adds another layer of precision. A question may concern cells, tissues, time, imaging, treatment pathways, or the environment around a tumour. A vaccine question may concern an immune response, pathogen, population, or public-health setting. These are serious but different objects of study. Marburg's mix of centres, platforms, and joint work is valuable when it helps a reader keep those objects distinct. The most useful next question is often the one that says exactly what is changing, how it could be observed, and why that change matters.

A Marburg topic gains shape when the system, scale, and method are specified

For Philipps-Universität Marburg, start by describing the system under study. It may be a cloud forest, enzyme, cell population, data model, medical process, climate pattern, or social practice around technology. Then set the scale. Is the question about a molecule, organism, laboratory group, landscape, institution, or wider population? Finally, identify a possible method or material: samples, microscopy, field observations, images, code, experiments, documents, interviews, or models. This prevents a topic from becoming a collection of scientific words without a clear direction.

The university's research centres and technology platforms can then help connect the system to a suitable setting. A profile area can provide a wider intellectual context, while a joint project can show how several disciplines approach a shared question. An early-stage group can introduce a focused line of work. Marburg becomes more navigable when it lets a reader move from a large concern to a question that can be described in terms of a system, a scale, and a way of learning about it.

Institution record

Country
Germany
Region
Europe
Status
Public
QS size code
L
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 opportunities

Opportunity records may use a different form of the institution's name. Confirm every listing with its original source.