Are you interested in shaping the future of global water modelling using AI?
The Research Associate in AI-Enabled Hydrological Modelling and Data Assimilation will develop innovative methods that combine large language models, data assimilation, and local knowledge to improve global water reanalysis. Working within an international, interdisciplinary team, the role contributes to cutting-edge research with real-world impact on water security and decision-making.
You will conduct cutting-edge research at the intersection of hydrology, data assimilation, and artificial intelligence, with a particular focus on integrating large language models and diverse knowledge sources into global water reanalysis systems. These systems provide comprehensive, retrospective reconstructions of the state of freshwater systems and are critical for understanding water availability and informing local and global decision-making. Your work will involve developing, testing, and applying novel computational methods to combine quantitative data with qualitative and semi-quantitative information, such as local expert knowledge and citizen science observations, to improve the robustness, actionability, and local relevance of water reanalysis products.
You will work closely with an international, interdisciplinary consortium, collaborating with researchers across environmental science, data science, and social science, and contributing to high-quality publications, open-source software, and project deliverables. You will also support the supervision of students and early career researchers, contribute to research funding proposals, and present your findings at internal meetings and international conferences.
You will be encouraged to take initiative in shaping your research direction, while working within a supportive team environment that values collaboration, reproducibility, and real-world impact.
We are looking for a motivated and collaborative researcher who brings a strong technical foundation alongside curiosity and initiative. In particular, you will have:
This role would suit someone who enjoys tackling complex methodological challenges and contributing to research with global relevance and real-world impact.
Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing
This is a full-time post (35 hours per week).
This role is for a fixed-term contract for 24 months. (with possibility of extension).
If you require any further details about the role, please contact: Rossella Arcucci - r.arcucci@imperial.ac.uk
To apply, please visit Jobs | Imperial College London and search for reference number: ENG03795
Closing Date: 4th March 2026
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