Research Fellow in Machine Learning for Environmental Modelling

Join to apply for the Research Fellow in Machine Learning for Environmental Modelling role at University of Leeds.

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 (http://glacial-lake-observatory.org/).

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:

We are a campus‑based community and regular interaction with campus is an expectation of all roles in line with academic and service needs and the requirements of the role. We are also open to discussing flexible working arrangements. To find out more about the benefits of working at the University and what it is like to live and work in the Leeds area visit our Working at Leeds information page.

As an international research‑intensive university, we welcome students and staff from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Within the Faculty/School of “Name” we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community; those who identify as LGBT+; and disabled people. Candidates will always be selected based on merit and ability.

Information for disabled candidates, impairments or health conditions, including requesting alternative formats, can be found on our Accessibility information page or by getting in touch with us at .

Select the following sentence for posts not covered by the Rehabilitation of Offenders Act:
A criminal record check is not required for this position. However, all applicants will be required to declare if they have any ‘unspent’ criminal offences, including those pending.
Any offer of appointment will be in accordance with our Criminal Records policy. You can find out more about required checks and declarations in our Criminal Records information page.