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PhD Scholarship 2025 in Geo Artificial Intelligence at University of Twente, Netherlands

Are you passionate about geo-artificial intelligence, climate change, and vegetation dynamics? The University of Twente in the Netherlands invites applications for the PhD Scholarship 2025 in Geo Artificial Intelligence for Mapping Vegetation Dynamics. This Dutch PhD opportunity is part of the EARTHONE Horizon Europe project, aimed at optimizing greenhouse gas removal by assessing sustainable land-use changes across different environments.

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About the Scholarship

As a PhD candidate, you will contribute to the EARTHONE (Environmental Analysis and Resilience for Transformative Human-Optimized Natural Environments) project by conducting advanced vegetation phenology studies at regional and local scales. This will involve integrating climate simulations, weather and remote sensing data, and ground observations from living labs in Southern Europe. Your focus will be on understanding how extreme weather events and land use changes affect vegetation seasonality and efficiency, using geo-artificial intelligence and spatiotemporal machine learning techniques.

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Scholarship Benefits

The University of Twente offers an excellent package for selected candidates, including:

  • Full-time PhD position for four years with a qualifier after 6–9 months.
  • Monthly gross salary: €2,901 in the first year, increasing to €3,707 in the fourth year.
  • Holiday allowance: 8% of gross annual salary and a year-end bonus of 8.3%.
  • 41 holiday days per year (full-time employment).
  • Professional development: Enrolment in the Twente Graduate School.
  • Pension scheme and excellent working conditions.
  • International work environment: A vibrant green campus and multicultural ecosystem.

Key Responsibilities

  • Conduct comprehensive phenology studies across Europe.
  • Model the impact of extreme weather and land use changes on vegetation.
  • Develop and apply advanced geo-AI methods and machine learning models.
  • Collaborate with climate modelers, foresters, and agronomists.
  • Work within an interdisciplinary and internationally oriented research environment.

Eligibility Criteria

To apply, candidates must meet the following requirements:

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  • MSc degree in geospatial, computer, environmental sciences, or related fields.
  • Strong programming skills (preferably in Python).
  • Experience with geospatial modelling, spatial statistics, and machine learning.
  • Ability to work on real-world interdisciplinary problems.
  • Excellent English communication skills (Dutch proficiency is a plus).
  • Background knowledge in climate change, ecology, or phenology is desirable.

Required Documents

Applicants must submit the following documents:

  1. Motivation letter: One-page explaining why you are interested in the topic and how you can contribute.
  2. Curriculum Vitae: Including attended courses, grades, references, and publications (if any).
  3. Research proposal: Two pages outlining your ideas on phenological modelling for the EARTHONE project.
  4. Description of your last significant project: About 250 words summarizing your MSc thesis or final project.

Application Timeline

Apply: https://utwentecareers.nl/en/vacancies/2032/phd-candidate-geo-artificial-intelligence-for-mapping-vegetation-dynamics/.

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The last date to apply for the PhD Scholarship 2025 in Geo Artificial Intelligence for Mapping Vegetation Dynamics offered by the University of Twente is March 5, 2025 and interviews are scheduled after March 19, 2025.

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Yousaf Saeed

Engr. Yousaf Saeed is a respected international education journalist, specializing in scholarships and fellowships. A recipient of three fully funded international scholarships in engineering, he brings firsthand experience to his work. With years of dedication to youth development across Asia, Africa, and beyond, Engr. Yousaf has helped thousands of students secure admissions, scholarships, and fellowships through accurate, experience-based guidance. All opportunities he shares are thoroughly researched and verified before publication.

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