PhD Position - Trustworthy AI and Causal Inference for Earth Observation

The Artifical Intelligence group is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series.

Currently, predictive AI in Earth Sciences relies heavily on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the failure (if they can), but they cannot explain why it happened or calculate the exact contribution of each individual stressor. Furthermore, these models often fail and make overconfident predictions when presented with unprecedented climate anomalies. We need a trustworthy AI for high-stakes environmental decisions.

In this research, you will learn how to move machine learning beyond pure correlation into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic, spatiotemporal Earth systems. Your primary focus will be time-series causal attribution: learning how to answer retrospective counterfactual questions to isolate the exact fraction of crop failure caused by specific stressors (e.g., ozone versus heat).

Importantly, your causal models will only be as reliable as the foundation model representations they rely on. Because extreme Earth system events are highly unpredictable, standard foundation models often output overconfident, flawed embeddings when faced with unprecedented climate anomalies. To solve this, you will also develop rigorous Out-of-Distribution (OOD) detection and Uncertainty Quantification (UQ) methods for Earth Foundation Models. By mathematically flagging when incoming data represents a never-before-seen anomaly, you ensure the foundation model does not pass "hallucinated" data to your causal model. This explicitly connects your uncertainty metrics to your causal outputs, ensuring the final system knows exactly when it is safely attributing causes and when it is extrapolating into the unknown.

You will apply your research directly to real-world compounding climate stress data from the Po Valley, working within the newly funded European project PROTEUS. Ultimately, the causal reasoning and uncertainty algorithms you build will serve as the quantitative engine for the "Copernicus Agent," an AI assistant designed to give European policymakers verifiable, data-driven post-mortem analyses of agricultural disasters.

Your duties and responsibilities include:

• Conduct research on state-of-the-art Causal Machine Learning for the Earth Sciences, Uncertainty Quantification, and Foundation Models for Earth observation.
  • Develop causal models using multimodal Earth Observation time-series to disentangle the effects of compounding climate extremes on crop yields.
  • Design robust uncertainty and OOD metrics for time-series satellite images and their foundation model embeddings to safely flag anomalies.
• Disseminate your research results by writing high-quality papers and presenting your findings at top-tier AI conferences (e.g., NeurIPS, CVPR, AAAI) and Earth Science journals.
• Collaborate closely with international partners in the PROTEUS project to integrate your causal attribution frameworks into the Copernicus Agent architecture.
• Contribute to a vibrant, interdisciplinary research lab working on causal machine learning for Earth and agricultural sciences, actively engaging in scientific discussions and joint research initiatives.

You will work here

The research is embedded within the chair Artificial Intelligence which is led by Prof. Ioannis Athanasiadis. You will be co-supervised by Dr. Vassilis Sitokonstantinou and Prof. Ioannis Athanasiadis.

You are highly motivated, self-driven, and curious to advance use-inspired artificial intelligence methods for Earth and environmental sciences. You have a strong intrinsic motivation for "AI for Good," driven by a desire to apply rigorous machine learning to pressing climate challenges. You bring along your enthusiasm to work in a highly dynamic, international team towards the common objective of building trustworthy, high-impact environmental intelligence.

You also possess: 

• 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. 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 programming in Python and experience with PyTorch, Scikit-Learn, or related modern machine learning libraries. Familiarity with collaborative coding environments (e.g., Git) and working on high-performance computing (HPC) clusters is an advantage.
• Good scientific writing and communication skills, as demonstrated by your thesis report or contributions to scientific papers.

For this position your command of the English language is expected to be at C1 level. Sometimes it is necessary to submit an internationally recognized Certificate of Proficiency in the English Language. More information can be found here.

Wageningen University & Research offers excellent terms of employment. A few highlights from our Collective Labour Agreement include:

• Partially paid parental leave;
• working hours that can be discussed and arranged so that they allow for the best possible work-life balance;
• there is a strong focus on vitality and you can make use of the sports facilities available on campus for a small fee;
• a fixed year-end bonus of 8.3%;
• excellent pension scheme.

In addition to these first-rate employee benefits, you will receive a fully funded PhD position and you will be offered a course program tailored to your needs and the research team.
The gross salary for the first year is € 3.059,- per month rising to € 3.881,-  in the fourth year in according to the Collective Labour Agreements for Dutch Universities (CAO-NU) (scale P). This is based on a full-time working week of 38 hours. We offer a temporary contract for 18 months which will be extended for the duration of the project if you perform well. 

We encourage development and internal mobility within our organisation. Our recruitment and selection policy sets out the conditions that apply specifically to you as a (former) employee. If you have any questions, we are happy to help.

You will work on the greenest and most innovative campus in the Netherlands, in an international and open working environment. 

Wageningen University & Research is the university and research centre for life sciences. The themes we deal with are relevant to everyone around the world and Wageningen, therefore, has a large international community and a lot to offer to international employees. 

Because we expect you to work and live in the Netherlands our team of advisors on Dutch immigration procedures will help you with the visa application procedures for yourself and, if applicable, for your family. 

Feeling welcome also has everything to do with being well informed. Wageningen University & Research's International Community page contains practical information about what we can do to support international employees coming to Wageningen. Furthermore, our Welcome Center can assist you with any additional advice and information about for example housing, opening a bank account, dual career programs or schooling. Finally, certain categories of international staff may be eligible for a tax exemption on a part of their salary during the first five years in the Netherlands.

For more information about the position, please contact Vassilis Sitokonstantinou, Assistant Professor, via email: vassilis.sitokonstantinou@wur.nl .
Questions about the procedure? Get in touch with Noorien Abbas, Corporate Recruiter, via vacaturemeldingen.psg@wur.nl .

Ready to apply?
You can apply directly using the apply button on the vacancy page on our website which will allow us to process your personal information with your approval. Only applications submitted through our website will be considered.

To apply, please send the following documents (max. 3 pages in total for both documents):

• Complete and up-to-date curriculum vitae;
• Motivation letter.
• Sample of your scientific writing (one paper, thesis, or report, that you have written yourself)

The maximum length of the documents must not exceed 3 pages. If it exceeds, applications will not be considered. Additional files such as grades and transcripts are not required during this stage and will not be considered.

You can apply up to and including May 11th, 2026. The first interviews are scheduled for May 26th 2026.

Your prompt response will ensure that your application continues to move forward in the evaluation process. We assure you that the information provided will be kept confidential and used solely for this application process.

Welcome, safe, and valued
Wageningen University & Research (WUR) highly values diversity and inclusion because we believe that different insights lead to innovative solutions. We create a work environment where everyone feels welcome, safe, and appreciated, regardless of background, identity, or experience. Together, we are building a culture where everyone's unique contribution adds to the success of our organization.

​The mission of Wageningen University & Research is “To explore the potential of nature to improve the quality of life”. Under the banner Wageningen University & Research, Wageningen University and the specialised research institutes of the Wageningen Research Foundation have joined forces in contributing to finding solutions to important questions in the domain of healthy food and living environment. With its roughly 30 branches, 7,600 employees (6,700 fte) and 13,100 students and over 150,000 participants to WUR’s Life Long Learning, Wageningen University & Research is one of the leading organisations in its domain. The unique Wageningen approach lies in its integrated approach to issues and the collaboration between different disciplines.

Read the 5 reasons why your future colleagues enjoy working at WUR and watch the video below to get an idea of our green campus!

We will recruit for the vacancy ourselves, so no employment agencies please. However, sharing in your network is appreciated.

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