Research Fellow in Machine Learning Environmental Modelling

Research Fellow in Machine Learning for Environmental Modelling

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Are you experienced in machine learning and looking to apply your skills to solve new challenges and reduce disaster risk? Do you want to further your career in one of the UK’s leading research‑intensive Universities? The University of Leeds is recruiting a post‑doctoral researcher to characterise and model the evolution of glacial lakes across High Mountain Asia.

We are looking for a Research Fellow to complete an important role in a UKRI Future Leaders Fellowship project:
Glacial Lake Observatory for Flood Hazards Impacted by Changing Climate (GLO‑FHICC) led by Dr C. Scott Watson. As glaciers disappear, thousands of glacial lakes are forming. Yet their location in high‑altitude and logistically challenging environments means observations are sparse, including essential measurements of water storage and potential flood hazards. This project aims to advance our understanding of glacial lake formation and glacier‑related flood hazards, with the goal of improving disaster preparedness and refining projections of glacier evolution across High‑Mountain Asia.

You will contribute to creating systematic and open access glacial lake monitoring through our Glacial Lake Observatory ((Use the "Apply for this Job" box below).).

In this role, you will develop innovative methods to quantify the morphology and evolutionary trajectories of glacial lakes across High Mountain Asia, by integrating multibeam sonar data, topographic information, and environmental variables with deep learning techniques. You will also contribute to refining digital elevation models (DEMs) and advancing flood hazard modelling in the complex topography of Himalayan catchments. You will work closely with Dr C. Scott

Watson and Dr Lauren Rawlins, alongside other project staff, students, and researchers in the Faculty. The position offers exciting opportunities to present your work at national and international conferences, and you will be supported in your professional development through funded training opportunities tailored to your career goals.

You will have, or be close to obtaining, a PhD in deep learning, computational geosciences, computer science, mathematics, or physics, and have experience of developing and applying deep learning models.

• Refining existing approaches to modelling glacial lake bathymetry and water storage;
• Integrating field measurements and satellite data to quantify glacier and glacial lake characteristics, and coupling these observations with a physics‑based framework to better understand their evolution;
• Developing and applying a deep learning modelling framework of glacial lake formation and evolution across Nepal and High‑Mountain Asia;
• Enhancing topographic data used in flood hazard modelling through deep learning techniques;
• Supporting and developing research activities, including the generation of independent and original ideas, advising on study design, and problem solving to ensure a successful programme of investigation;
• Preparing papers and datasets for publication in leading international journals;
• Working both independently and as part of the research group. You will have opportunities to present research outputs at national and international conferences, and support knowledge‑transfer activities with project partners in Nepal;
• Collating, analysing, and presenting data and figures to inform the direction and progression of the project;
• Contributing to the development of further research funding proposals;
• Support the research culture of the School, including training and mentoring undergraduate and/or postgraduate students in areas relevant to the project;
• Continually updating your knowledge, understanding and skills in the research field.
• These duties provide a framework for the role and should not be regarded as a definitive list. Other reasonable duties may be required consistent with the grade of the post.

Dr C. Scott Watson
UKRI Future Leaders Fellow
Email: