PhD Candidate Geo artificial intelligence for mapping vegetation dynamics

The University of Twente wants to be an organisation that effectively deploys diversity, talents, and capabilities in the labour market for now and in the future. In the framework of our diversity and inclusiveness policy, we strongly stimulate people with a (work) disability to apply for this position.

The University of Twente is committed to providing a working environment where everyone is valued, respected, and supported to progress. Our priority is to ensure that no one is disadvantaged based on their ethnicity, gender, culture, disability, LGBTQ+ identities, family and caring responsibilities, age, or religion. We encourage everyone who shares these values to apply.

Your challenge
You will help us realizing the ambitions of the EARTHONE (Environmental Analysis and Resilience for Transformative Human-Optimized Natural Environments) Horizon Europe project from a geospatial perspective.

EARTHONE will contribute to optimizing the net removal of Greenhouse gas emissions by assessing the contribution of sustainable land-use changes in different environments. To achieve this, EARTHONE will follow a multidisciplinary and multiscale approach, integrating innovative data-driven technologies with regional context through a series of living labs in Southern Europe.

You will conduct a comprehensive vegetation seasonality (phenology) study at regional and local scales to better understand how vegetation responds to climate change by integrating and analysing climate simulations, weather and remotely sensed products, and ground observations collected from the existing networks and the project living labs. You will start by mapping the main phenological regions in Europe. Then you will focus on modelling the role of extreme weather events and land use change on vegetation seasonality and efficiency. For this, you will develop and apply geo-artificial intelligence methods, including spatiotemporal machine learning techniques, and gather and integrate experienced knowledge from climate modellers, foresters, soil scientists and agronomists.

• An MSc degree in geospatial, computer, or environmental sciences, or any other related field
• A strong passion for data-driven analysis coupled with excellent programming skills (preferably Python) and knowledge of and experience with the design and implementation of machine learning models
• A background in geospatial modelling, spatial statistics and machine/deep learning
• The ability to work on real-world problems in an interdisciplinary and internationally oriented environment
• Good communication skills and an excellent command of English. Fluency in Dutch or willingness to learn Dutch is a plus
• Climate change and ecological and/or phenological knowledge are a plus

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

• An inspiring multidisciplinary, international, and academic environment. The university offers a dynamic ecosystem with enthusiastic colleagues, and internationalization is an important part of the strategic agenda.
• Full-time position for four years with a mandatory qualifier after 6–9 months.
• A professional and personal development programme within Twente Graduate School
• Gross monthly salary of € 2,901.00 in the first year, which increases to € 3,707.00 in the fourth year
• A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%
• Excellent support for research and facilities for professional and personal development
• A solid pension scheme
• A total of 41 holiday days per year in case of full-time employment
• Excellent working conditions, an exciting scientific environment, and a lively green campus

For more information about the PhD programme at UT, check the UT PhD Information Page.

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