PhD Studentship in CACP: Understanding predictability and making predictions in Sub-seasonal to Seasonal (S2S) time scale using machine learning

  • Subject area: Physics, Atmospheric/Climate Science, Data Science
  • Location/Campus:College Lane Campus, Hatfield
  • Start date: 01 September 2023
  • Closing application date: 15 February 2023
  • Duration of Contract: three year fully funded PhD on a full-time basis
  • Hours: full-time
  • Interviews start date: week commencing 20 February 2023

Project outline

Sub-seasonal to Seasonal (S2S) climate forecasts suffer from a significant lack of prediction skill beyond week-two lead times, yet reliable and actionable information on these timescales are required for decision making in many sectors. Over the past decade, there has been a substantial research effort to improve prediction on these timescales, with the potential to fill the gap between weather forecasts. However, the skill of the forecast is still limited.

Recently, there is significant interest to explore Artificial Intelligence/Machine Learning Framework to improve the forecast skill in this scale by understanding the un-explored predictability (beyond linear framework) and using a non-linear approach for more reliable forecasts. his research project will explore the predictability in sub-seasonal scale and further improve the skill using the following approaches:

  • Understanding Predictability using Explainable Artificial Intelligence (XAI) in sub-seasonal time scale.
  • Calibration of S2S and Sub-X model outputs using AI/ML based technique for Probabilistic predictions for tercile categories.
  • Probabilistic Multi-model ensemble (PMME) using S2S and Sub-X model outputs for tercile categories.
  • Constructing PMME for the full probability distribution to predict extreme events (Prob. of Exceedance).


Entry requirements

Applications are invited from individuals with a Bachelor’s degree (2.1 or above) and/or Master (e.g. if your BSc degree classification is 2.2) in a relevant subject e.g. Physics, Meteorology, Atmospheric/Climate Science, Data Science, Remote Sensing, Mathematics, Computer Science or Engineering.

We are seeking applicants with:

  • Experience in data analysis and numerical modelling (expected but not mandatory)
  • Programming skills with at least one of the following programming languages: Python, FORTRAN, C/C++, MATLAB, IDL
  • Overseas applicants to have an IELTS (English proficiency) score of 6.5 or above (if they get selected for the studentship)
  • This project is a combination of both experimental and computational work. We welcome applications from candidates who have experience in or are willing to support lab/field measurements and modelling.


Open to both UK and International applicants, including EU applicants.

How to apply

Applicants must provide the following:

  • a completed application form (PDF - 0.19 Mb)
  • a research proposal (not exceeding two A4 pages)
  • a  CV
  • a cover letter
  • two academic references to be provided by referee directly to the doctoral college
  • copies of qualification certificates and transcripts
  • certification of English language competence (minimum IELTS 6.5 or equivalent) for candidates for whom English is not their first language.

Please email completed applications to the Doctoral College Admissions team by 15 February 2023.

For informal enquires please email Ms Lynette Spelman, or Dr. Pushp Raj Tiwari using the subject line: CACP PhD Studentships.


Annual tax-free bursary of approximately £17,668, plus tuition fees (£5,590 for UK or £ 14,905 for International and EU applicants).