University:
Delft University of Technology
Country:
Netherlands
Deadline:
2025-09-26
Fields:
Environmental Science, Environmental Engineering, Hydrogeology, Applied Mathematics, PhysicsAre you passionate about combining computational modeling, machine learning, and environmental sciences to address pressing challenges in groundwater quality and contamination? If you are eager to advance your academic career while working at the intersection of hydrogeology, statistics, and artificial intelligence, this PhD position at Delft University of Technology could be the perfect next step for you.
About The University Or Research Institute
Delft University of Technology (TU Delft) is one of Europe’s most renowned technical universities, consistently ranked among the top institutions worldwide for engineering and technology. Located in the historic city of Delft, Netherlands, TU Delft is celebrated for its cutting-edge research, vibrant international community, and strong industry partnerships. The Department of Water Management at TU Delft is a leader in water-related research, integrating expertise from environmental engineering, hydrology, and computational sciences to tackle global water challenges.Studying in the Netherlands offers students a unique blend of high-quality education, multicultural experiences, and excellent work-life balance. The country is known for its innovative research environment, open and collaborative academic culture, and strong emphasis on sustainability and societal impact. TU Delft’s central location in Europe also provides easy access to a wide network of academic and industrial partners, making it an ideal place to launch an international research career.
Research Topic and Significance
The core focus of this PhD project is the development of surrogate modeling techniques to enable efficient uncertainty quantification in reactive transport modeling of groundwater systems. Groundwater quality is a critical concern worldwide, with implications for drinking water safety, agricultural productivity, and ecosystem health. Reactive transport models are essential tools for predicting the fate of contaminants and nutrients in subsurface environments, but their computational demands often limit the scope and reliability of uncertainty analyses.
Also See
- PhD Positions in Climate and Groundwater Research at UNSW Sydney
- Canada – PhD/Postdoc in Numerical Optimization at Polytechnique Montréal
- PhD Opportunity in Symbolic AI and Reasoning Under Uncertainty at TU Delft
- Germany – PhD in Hydrological Modelling with Machine Learning at University of Stuttgart
- Canada – PhD in Applied Mathematics at University of Waterloo
By leveraging advanced surrogate modeling and machine learning approaches, this project aims to make uncertainty quantification more tractable and accurate, thereby improving predictions of groundwater contamination risks. The research will not only contribute to scientific understanding but also support better decision-making in environmental management and policy. The project’s interdisciplinary nature—spanning hydrogeology, biogeochemistry, statistics, and AI—reflects the complex, real-world challenges facing water resources today.
Project Details
This Four-year PhD Position Is Based At The Department Of Water Management At TU Delft. The Successful Candidate Will Work Closely With AIdroLab At TU Delft AI And Collaborate With The Statistical Model-Data Integration Group At The University Of Stuttgart, Germany. The Project Involves– Designing and implementing surrogate modeling strategies for complex reactive transport models.– Evaluating and propagating surrogate model errors to enhance the robustness of uncertainty quantification.– Applying the developed methodologies to real-world case studies, often in collaboration with international research partners.The position offers a competitive monthly salary (in accordance with Dutch Universities CAO) and the opportunity to work in a stimulating, interdisciplinary environment with access to state-of-the-art computational resources.
Candidate Profile
The Ideal Applicant Will Have
– A Master’s degree in environmental science, environmental engineering, hydrogeology, physics, applied mathematics, or a related field.– A strong interest in uncertainty quantification, Bayesian statistics, and machine learning.– Demonstrated skills in numerical modeling and scientific programming (Python, Julia, or R).– Preferably, experience with reactive transport modeling in groundwater systems.– Excellent English communication skills, both written and spoken.– A passion for interdisciplinary research and international collaboration.This position is particularly suited for candidates who are eager to bridge environmental sciences with computational and statistical methods, and who thrive in collaborative, cross-disciplinary research settings.
Application Process
To Apply, Candidates Should Prepare The Following Documents
– Motivation letter (maximum 1 page)– Curriculum Vitae (1–2 pages), including contact details of two references– Academic transcripts, including grades– An example of a scientific text authored by the applicant (e.g., MSc thesis)Applications must be submitted online via the official TU Delft careers portal:https://careers.tudelft.nl/job/Delft-PhD-Position-Surrogate-enabled-Uncertainty-Quantification-for-Reactive-Transport-Modeling-2628-CD/805694202/The application deadline is 26 September 2025. Interviews will be held in early October 2025. Please refer to the official advertisement for further details on the application process.
Conclusion
This is an outstanding opportunity for aspiring researchers who want to make a meaningful impact on environmental science and groundwater management through advanced computational techniques. If you are ready to contribute to international collaborations and develop innovative solutions for real-world challenges, we encourage you to apply for this PhD position at TU Delft. Stay tuned for similar openings and further your academic journey in one of Europe’s most dynamic research environments.Want to calculate your PhD admission chances? Try it here:https://phdfinder.com/phd_admission_chance_calculator/
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