Publisher
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Wageningen University & Research

PhD in Trustworthy AI and Causal Inference for Earth Observation Wageningen University & Research in Netherlands

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD position with tailored training. Gross salary is €3,059 per month in the first year, rising to €3,881 per month in the fourth year, plus an annual 8.3% year-end bonus. Visa and relocation support are offered. Temporary contract for 18 months, extendable for the project duration if performance is good.

Deadline

Expired

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Country

Netherlands

University

Wageningen University & Research

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Keywords

Computer Science
Environmental Science
Agriculture
Information Technology
Remote Sensing
Time Series Analysis
Climate Science
Earth Science
Python Programming
Uncertainty Analysis
Causal Inference
Earth Observation
Crop Yield
Statistics

About this position

PhD opportunity at Wageningen University & Research in the Netherlands on Trustworthy AI and Causal Inference for Earth Observation. The project sits in the Artificial Intelligence chair and is embedded in the European project PROTEUS, with applications to climate extremes, agricultural disasters, and satellite-based Earth observation.

You will work on causal machine learning, uncertainty quantification, out-of-distribution detection, and time-series analysis for multimodal remote sensing data. The research aims to explain crop failure and disentangle the effects of compounding stressors such as heat, drought, and pollution, while building trustworthy AI methods for high-stakes environmental decision-making.

Supervision is by Prof. Ioannis Athanasiadis and Dr. Vassilis Sitokonstantinou. The post is based in Wageningen, Netherlands, and includes a fully funded PhD position, tailored training, visa and relocation support, and a salary from €3,059 to €3,881 per month plus an 8.3% year-end bonus.

Eligibility highlights include an MSc in AI, statistics, computer science, remote sensing, engineering, or a related field; applied machine learning experience; strong interest in probabilistic ML, causal inference, or time-series; Python and PyTorch/Scikit-learn skills; and English at C1 level. Experience with Earth observation data, Git, and HPC is advantageous.

To apply, submit a CV, motivation letter, and one sample of scientific writing, with a maximum of 3 pages total. Transcripts are not required at this stage. The deadline is 11 May 2026, and first interviews are scheduled for 26 May 2026.

Funding details

Fully funded PhD position with tailored training. Gross salary is €3,059 per month in the first year, rising to €3,881 per month in the fourth year, plus an annual 8.3% year-end bonus. Visa and relocation support are offered. Temporary contract for 18 months, extendable for the project duration if performance is good.

What's required

A successfully completed MSc degree in artificial intelligence, statistics, computer science, remote sensing, engineering, or a similar relevant field. Demonstrated experience in applied machine learning is required. A background or strong interest in probabilistic machine learning, causal inference, or time-series analysis is highly desirable. Experience with Earth observation or remote sensing data is a strong plus. Proficiency in Python and experience with PyTorch, Scikit-Learn, or related modern machine learning libraries is required. Familiarity with Git and HPC clusters is an advantage. Good scientific writing and communication skills are expected. English proficiency at C1 level is required, and an internationally recognized certificate may be requested.

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