Research Fellow in AI for Climate Modelling
| Posting date: | 18 December 2025 |
|---|---|
| Salary: | £41,064 to £44,746 per year |
| Hours: | Full time |
| Closing date: | 10 January 2026 |
| Location: | Southampton, Hampshire |
| Remote working: | On-site only |
| Company: | University of Southampton |
| Job type: | Contract |
| Job reference: | 3294325HN |
Summary
About the Role
We are seeking to recruit a Research Fellow in AI for Climate Modelling to become a member of the School of Ocean and Earth Science at the University of Southampton. The successful candidate will be part of the ‘Accurate projections of climate recovery from a combination of historical data, simple models and AI’ Future Leaders Fellowship project, which seeks to develop new modelling methods that combine historical climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future.
As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing, testing, evaluating, and iterating the model, you will engage with stakeholders to design user-friendly interfaces that maximise impact. The successful applicant will join an active ocean and climate physics group at the University of Southampton. You will be part of a vibrant oceanographic and climate science community at the School of Ocean and Earth Science and the National Oceanography Centre, working closely with national and international partners. We are strongly committed to developing the next generation of climate-modelling tools.
The post is a fixed-term appointment for 36 months (3 years), with the possibility of extension for up to one additional year, and is part of a UKRI Future Leaders Fellowship project awarded to Dr Duo Chan.
About You
Given the interdisciplinary nature of the post, we welcome applications from candidates with a PhD (or equivalent) in Artificial Intelligence/Machine Learning, or climate science with substantial experience applying advanced AI methods to climate problems. We are particularly interested in researchers who develop or apply state-of-the-art ML/AI approaches — such as deep learning, generative models, or equation discovery — rather than traditional statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in HPC or cloud environments. Strong quantitative problem-solving and communication skills are also essential.
We welcome applications from all interested candidates and particularly encourage those who identify as Black, Asian, from a minority ethnic or lower socio-economic background, LGBTQ+, women, or people with disabilities, as these groups are currently underrepresented in our community.
What we can offer you
We are an ambitious, supportive academic community committed to helping everyone reach their full potential in education, research, and enterprise. We hold a departmental Athena SWAN Bronze award and an institutional Silver award. We value diversity, equality, and flexible working, and we consider career breaks for reasons such as parental leave, disability, or illness. We offer a generous holiday allowance, additional closure days, subsidised health and fitness facilities, and membership of the Universities Superannuation Scheme (USS). We also support early career researchers in line with the Concordat to Support the Career Development of Researchers.
Enquiries
For informal inquiries please contact Dr. Duo Chan (Duo.Chan@soton.ac.uk).
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or recruitment@soton.ac.uk quoting the job number.
We are seeking to recruit a Research Fellow in AI for Climate Modelling to become a member of the School of Ocean and Earth Science at the University of Southampton. The successful candidate will be part of the ‘Accurate projections of climate recovery from a combination of historical data, simple models and AI’ Future Leaders Fellowship project, which seeks to develop new modelling methods that combine historical climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future.
As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing, testing, evaluating, and iterating the model, you will engage with stakeholders to design user-friendly interfaces that maximise impact. The successful applicant will join an active ocean and climate physics group at the University of Southampton. You will be part of a vibrant oceanographic and climate science community at the School of Ocean and Earth Science and the National Oceanography Centre, working closely with national and international partners. We are strongly committed to developing the next generation of climate-modelling tools.
The post is a fixed-term appointment for 36 months (3 years), with the possibility of extension for up to one additional year, and is part of a UKRI Future Leaders Fellowship project awarded to Dr Duo Chan.
About You
Given the interdisciplinary nature of the post, we welcome applications from candidates with a PhD (or equivalent) in Artificial Intelligence/Machine Learning, or climate science with substantial experience applying advanced AI methods to climate problems. We are particularly interested in researchers who develop or apply state-of-the-art ML/AI approaches — such as deep learning, generative models, or equation discovery — rather than traditional statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in HPC or cloud environments. Strong quantitative problem-solving and communication skills are also essential.
We welcome applications from all interested candidates and particularly encourage those who identify as Black, Asian, from a minority ethnic or lower socio-economic background, LGBTQ+, women, or people with disabilities, as these groups are currently underrepresented in our community.
What we can offer you
We are an ambitious, supportive academic community committed to helping everyone reach their full potential in education, research, and enterprise. We hold a departmental Athena SWAN Bronze award and an institutional Silver award. We value diversity, equality, and flexible working, and we consider career breaks for reasons such as parental leave, disability, or illness. We offer a generous holiday allowance, additional closure days, subsidised health and fitness facilities, and membership of the Universities Superannuation Scheme (USS). We also support early career researchers in line with the Concordat to Support the Career Development of Researchers.
Enquiries
For informal inquiries please contact Dr. Duo Chan (Duo.Chan@soton.ac.uk).
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or recruitment@soton.ac.uk quoting the job number.
Proud member of the Disability Confident employer scheme
Disability Confident
About Disability Confident
A Disability Confident employer will generally offer an interview to any applicant that declares they have a disability and meets the minimum criteria for the job as defined by the employer. It is important to note that in certain recruitment situations such as high-volume, seasonal and high-peak times, the employer may wish to limit the overall numbers of interviews offered to both disabled people and non-disabled people. For more details please go to Disability Confident.