{
  "id": 12565476,
  "title": "Climate Change Scholarship Opportunities in UK for Pakistani Students",
  "url": "https://urgent.news/2026/10/07/climate-change-scholarship-opportunities-in-uk-for-pakistani-students",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-07T05:57:13.000Z",
  "source": {
    "name": "ProPakistani",
    "slug": "propakistani",
    "url": "https://propakistani.pk/2026/10/07/climate-change-scholarship-opportunities-in-uk-for-pakistani-students/"
  },
  "original_language": "en",
  "account": "Two fully-funded PhD studentships at University College London (UCL) are available for Pakistani students interested in climate change research. The NERC-funded UNRISK Centre for Doctoral Training offers opportunities under two distinct projects for the 2027 intake. Both projects provide full tuition coverage, a UKRI maintenance stipend, and dedicated research and training support.\n\nThe first project, \"Reducing Uncertainty in the Impact of Climate on Biodiversity,\" aims to improve predictions about how climate change affects biodiversity. Researchers will utilize long-term data from fossil records, museum collections, and historical surveys to enhance predictions, focusing on marine biodiversity such as arthropods and corals. The project employs computational methods like text and image mining to analyze historical data and assess its impact on climate-related biodiversity predictions.\n\nApplicants for this project should have backgrounds in environmental science, Earth sciences, ecology, biology, palaeontology, computer science, or related fields. Strong quantitative and computational skills are advantageous, and familiarity with R or Python, statistics, programming, data analysis, or machine-learning techniques would be beneficial. Previous experience with fossils is not mandatory.\n\nThe second project, \"Robust and Scalable Spatio-Temporal Modelling,\" focuses on developing advanced statistical and machine-learning methods for climate prediction. Climate models often struggle with incomplete, irregular, or error-prone data, which can affect prediction accuracy. This project aims to create mathematical and computational approaches that remain reliable despite imperfect observations or modeling assumptions. Researchers will explore techniques such as Gaussian processes, Kalman filtering, Bayesian inference, robust statistics, and scalable machine-learning methods.\n\nApplicants for this project should possess strong backgrounds in mathematics, statistics, computer science, machine learning, or related disciplines. Proficiency in programming and interest in mathematical and computational research are essential. While experience in climate science is not essential, candidates should demonstrate a genuine interest in the subject.\n\nThe UNRISK Centre provides comprehensive financial support, covering full university tuition fees and a UKRI maintenance stipend of £23,805 per year for three years and nine months. Each student receives a £6,000 individual Research Training and Support Grant, along with approximately £5,000 in cohort-level training per student. Additionally, an expected three-month placement is included in the funding period to enhance the researcher's experience beyond academic work. Pakistani students are eligible to apply as international candidates, with applicable tuition fees covered alongside the stipend and research support. However, the number of international-funded places is limited under UKRI funding rules.",
  "summary": "Pakistani students can apply for two fully-funded PhD studentships at University College London (UCL) under the NERC-funded UNRISK Centre for … Read More The post Climate Change Scholarship Opportunities in UK for Pakistani Students appeared first on ProPakistani .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}