Research Fellow in African Sub-seasonal Weather Prediction
This role will be based on the university campus, with scope for it to be undertaken in a hybrid manner. We are also open to discussing flexible working arrangements.
Are you an atmospheric scientist looking to apply your expertise to real-world forecasting challenges in Africa?
Machine-learning has the potential to revolutionise weather prediction in Africa, and we are seeking a scientist who understands and enjoys challenges in atmospheric and climate dynamics, weather prediction and predictability. You will take a lead on the deployment and evaluation of a new generation of machine learning-based sub-seasonal weather forecasts for African agriculture.
The Cumulus project is a consortium of UK and African partners funded by the Gates Foundation, which aims to make a breakthrough in the application of machine-learning forecasting methods for West African agriculture. The project is led by the UK’s Alan Turing Institute, with partners in Senegal and Ghana, and all partners will collaborate closely. We will also be part of an over-arching project – Nimbus – linking with US and East African teams and other international specialists.
Within Cumulus, you will lead the application and evaluation of sub-seasonal (2-4 week) forecasts. Other members of the team will be developing innovative machine-learning methods for global sub-seasonal prediction and downscaling for Africa. We aim to get the first models developed rapidly, and you will support work to ensure that the methods can be run, evaluated and improved by partners in African universities and weather services.
A significant part of your work, in collaboration with the African groups, will be to understand how to create and evaluate forecasts of highest priority to farmers (such as rainfall onset prediction) from the machine-learning derived products. We aim to understand the predictability of these forecasts as a function of lead time, spatial scale and the controlling physical processes or phenomena. You will also lead on the evaluation of the forecasts according to known physical drivers and constraints, such as tropical wave modes, feedback with the land surface and response to global sea surface temperatures. From these insights into climate dynamics in the machine-learning predictions, we aim to understand drivers of predictability: are there “windows of opportunity” of high predictive skill which may benefit farmers?
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa .
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent .
What we offer in return
•26 days holiday plus approx. 16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!
•Generous pension scheme plus life assurance – The University contributes 14.5% of salary.
•Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
•Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
•Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
If you are looking for a role where you develop real-world impact from your climate dynamics expertise, apply today.
To explore the post further or for any queries you may have, please contact:
#J-18808-Ljbffr