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Universitas Trento

Universitas Trento Market Share Analysis for April 2026

Universitas Trento merupakan sebuah perguruan tinggi di kota Trento, Italia yang dalam waktu kurang dari 50 tahun berhasil mencapai hasil yang sempurna dalam pendidikan, penelitian dan hubungan internasional.

Science and Education > Education

Domainunitn.it

Year Founded--

Employees1001 - 5000

Annual Revenue--

Universitas Trento

Universitas Trento (including company regionals)

unitn.it

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Total visits to Universitas Trento's top domains

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Total visits last 3 months

Subsidiaries Breakdown

unitn.it1.4M

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Avg. visit duration in Universitas Trento's top domains

Analyze Universitas Trento engagement metrics.

Average visit duration last 3 months

Subsidiaries Breakdown

unitn.it00:04:05

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Avg. pageviews on Universitas Trento's top domains

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Average page views last 3 months

Subsidiaries Breakdown

unitn.it5.95

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Universitas Trento top competitors by domain

Top similar sites in April 2026, ranked by their affinity

univr.itCompany NameUniversitas VeronaEmployees1001 - 5000HQItaly, VeronaAnnual revenue< $1MTotal visits1.3MScience and Education > Education

Top technologies used by Universitas Trento

These are the website technologies, by industry, used by Universitas Trento top domains

Payment & Currencies (25)

PayPal

Content Management System (4)

WordPress

Advertising (3)

DoubleClick

Audio Video Media (3)

YouTube

More Technologies

8

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News & Signals from Universitas Trento

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NewsUnitn partners with Deda Cloud S.R.L..The project was developed by a public-private partnership between the University of Trento and local businesses, including construction firm Covi Costruzioni, IT services company Dedagroup, healthcare provider GPI, and holding company Istituto Atesino di Sviluppo.

NewsCOSBI - Piazza Manifattura partnered with Unitn on Sep 10th '25.At CIBB 2025, held at Politecnico di Milano from September 10 to 12, Stefano Giampiccolo, PhD student at COSBI in collaboration with the University of Trento, presented his research on "Uncertainty quantification in out-of-distribution scenarios for data-driven and hybrid neural-network/mechanistic modeling in computational Systems Biology."