PhD Candidate in Explainable AI for Earth Observation

With the growing integration of Artificial Intelligence (AI) in geospatial decision-making processes, ensuring transparency, accountability, and trust in model outputs has become imperative, especially in critical domains such as disaster risk management, social vulnerability mapping using satellite imagery, and ecological monitoring of invasive species.

You will explore how Explainable AI (XAI) methods can efficiently interpret geospatial models' behaviour. The central aim is to explore how such techniques can provide intuitive, human-interpretable explanations for complex model predictions derived from Earth Observation data, including high-resolution satellite and aerial imagery.

You will engage in a comparative analysis of XAI techniques in two key directions: their capacity to enhance the interpretability and usability of model outputs for non-technical stakeholders—partners, such as policy-makers, environmental managers, and disaster response coordinators—including the potential integration of Large Language Models (LLMs) to translate technical explanations into more accessible, natural language narratives — and their potential to iteratively improve model performance by integrating feedback from explanation-based diagnostics.

The research may be applied to diverse domains, ranging from damage assessment in post-disaster scenarios to detecting environmental change processes in vulnerable ecosystems.

You will present (both preliminary and mature) results at appropriate meetings and conferences, publish results in scientific workshops, conference proceedings, and journals, contribute to teaching on topics related to your work, and supervise and mentor involved MSc students as needed.

• An MSc degree in Geoscience, Computer Science, Artificial Intelligence, Mathematics, or a related field
• A curiosity in deep learning, image processing, GIS, and a willingness to delve deeper into these topics
• Passion for research and a background in geospatial modelling, machine learning, with programming experience in Python
• The ability to work on real-world problems in an interdisciplinary and internationally oriented environment
• Excellent communication skills and a strong command of the English language. Fluency in Dutch or willingness to learn Dutch is a plus

Interested and motivated candidates are encouraged to apply, even when they still need to obtain all the desired skills. You can develop relevant skills on the job through dedicated learning and doctoral training.

The Geo-Information Processing Department (GIP) works on the design and development of methods & techniques for processing (acquiring, organising, analysing) heterogeneous collections of spatiotemporal data, and in the implementation of open geo-information solutions (models, visualisations, and services) that help to understand key societal problems.

For more information about the position, you can contact Dr Caroline Gevaert (email: c.m.gevaert@utwente.nl) or Prof Raúl Zurita-Milla (email: r.zurita-milla@utwente.nl). You are also invited to visit our homepage.

For questions about working and living in the Netherlands, please consult the official website of the Netherlands Government or the website of the Expat Centre East Netherlands.

Screening is part of the selection procedure.

Please submit your application before 27 July 2025 at 17:00 CET. Your application should include:

• A motivation letter (maximum one page of A4), emphasizing your specific interest, qualifications, and motivation to apply for this position
• Curriculum Vitae (including a list of all courses attended, grades obtained, the names and contact information of two references and – if applicable – a publication list)
• A short description (maximum 1 page of A4) of a research proposal outlining your ideas on further exploring explainable GeoAI in the context of the vacancy text.
• A short description (about 250 words) of your last significant result (MSc thesis/final project report/EngD thesis).

These last two points may be included in the same document as your motivation letter. If you use any AI tools in preparing these documents, please state this at the end of each relevant document.

First-round (online) interviews are scheduled for August 12, 2025. A (possible) second round interview might take the week of August 25th.