New UROP opportunities will be listed here for one month, and thereafter will appear on the relevant faculty page (UROP website) until notified otherwise by the relevant member of academic staff.

There are currently two UROPs being advertised on this page.

NEW: 29 June

Downscaling physical risks from global climate models: The project aims to assist a team of researchers in the projection of physical risks along different climate pathways with application to forests. Projection of physical risks at high spatial resolutions will allow us to understand the evolution of carbon sequestration potential and vulnerability to different risks including wildfires. The project is of relevance to students interested in climate risk analytics (physical risk projections and model uncertainty), carbon-based assets (such as forestry-linked securities) and the integrated modelling of various risks (e.g., wildfire risk and carbon emissions).

Skills and experience required: Python, statistics, machine learning

Further information and how to apply:

  • The Leverhulme Centre for Wildfires Environment and Society welcomes applications for an 8-week summer project on the topic of Wildfires versus Forest Fragmentation.
  • This UROPs is offered on a remote or hybrid basis. On-campus attendance will be possible, but it will not be essential.
  • A bursary is available, of £319/week to the successful applicant.
  • To apply, please download an application form [external site] and email the completed form along with your CV to Dr Enrico Biffis, Dr Giuseppe Brandi, and Dr Kaveh Salehzadeh Nobari by Wednesday 13th July, 5pm BST - e.biffis@imperial.ac.uk; g.brandi@imperial.ac.uk; k.salehzadeh-nobari@imperial.ac.uk.
  • Please check the Imperial College London UROP pages for general information and eligibility. Applications are especially welcomed by students of Black heritage, as well as from individuals who are members of current and historically underrepresented groups.

 

NEW: 27 June

Wildfire versus Forest Fragmentation: Most biomes are adapted to regular fire or repeated burning especially grassland, savanna (open and woody savannas) and dry forest in comparison to tropical rainforest. This is because wildfire is a natural process which is as ancient as plant kingdom. In some biomes of the tropics, wildfire would be more frequent without human activities; while others would be unchanged, or less frequent. For example, in the Amazon basin a positive relationship between fragmentation and burnt area in fire-free evergreen broadleaf forests while decreased fire in fire-adapted savannas has been observed (below figure) (Harrison et al. 2021).

Recent research has numerically demonstrated that efficient nowcasting of fire burnt area can be obtained by using local geological and climate features (e.g., vegetation, slope, wind direction) as model inputs (below figure) (Cheng et al. 2022).

Methodology: In this research, the spatio-temporal relationship between burnt area/ fire frequency and forest fragmentation in the selected tropical biome across the Amazonia, Africa and southeast Asia would be explored. These sites have different fire regimes based on different vegetation types (evergreen forest, savanna forest, mixed peat swamp forest) with different fire frequency in different years. This will involve long-term multispectral Landsat and Sentinel-2 and pan-tropical forest fragmentation datasets developed by Hansen et al. (2020). Furthermore, forest fragmentation will also use as inputs in machine learning algorithms to enhance the current burnt area/ fire frequency predictions. As a first step, image-based machine learning algorithms will be employed to inspect the impact of forest fragmentation on fire propagation/fire duration.

Skills and experience required: Ecology, analysis skills, Python programming, initiative as part of a team.

Further information plus how to apply:

  • The Leverhulme Centre for Wildfires Environment and Society welcomes applications for an 8-week summer project on the topic of Wildfires versus Forest Fragmentation.
  • This UROP is offered on a remote or hybrid basis by the Dept of Life Sciences. On-campus attendance will be possible, but it will not be essential.
  • A bursary is available, of £319/week to the successful applicant.
  • To apply, please download an application form [external site] and email the completed form along with your CV to Dr Ramesh Ningthoujam (Dept of Life Sciences) and Dr Sibo Cheng (Dept of Computing) at rningtho@ic.ac.uk AND sibo.cheng@ic.ac.uk by Monday 11th July, 5pm BST.
  • Please check the Imperial College London UROP pages for general information and eligibility. Applications are especially welcomed by students of Black heritage, as well as from individuals who are members of current and historically underrepresented groups.
  • The participant will be co-supervised by Dr Ramesh Ningthoujam, Dr Sibo Cheng, Dr Rossella Arcucci and Prof Colin Prentice. The output will be useful in understanding how wildfires affect fragmentation process and vice-versa at landscape level, developing fire regime definition for the tropics in the LCWES and present results in upcoming EGU2023 Conference in Vienna.

Further readings:

  • Cheng, S., et al. (2022). Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting. Journal of Computational Physics, page 111302.
  • Hansen, M. C., et al. (2020). The fate of tropical forest fragments Sci. Adv. 6 eaax8574.
  • Harrison, S.P., et al. (2021). Wildfire regimes: an ecological perspective, Environ. Res. Lett. 16 125008.