Transient carbon sequestration, such as in nature-based projects, can be viewed as a process of carbon deposition into the natural environment by means of an arrival process and its departure by means of a departure process. Arrival processes include tree and vegetation growth, the acquisition of carbon by soil fungi, and phytoplankton blooms. Departure processes include forest fires, biomass decomposition, and forest logging. When viewed using this lens, the amount of carbon being sequestered is a queue that arises due to the inherent mismatch between arrival and departure rates. Thus, characterizing the evolution of the queue, for example, its mean length and the probability of the queue declining to zero (i.e., a complete depletion of carbon) are fundamental in understanding the process.
The goal of this project is to use techniques from queueing theory to model and analyse nature-based solutions. Data analysis will be used to characterize realistic arrival and departure processes, and both queueing theory and simulation will be used to determine the appropriate properties of the queue.
Useful skills for this project include:
- Prior work in mathematical modelling, particularly queueing theory
- Experience in data analysis and data science
- Experience with simulation of physical systems
- An interest in modelling the complexities of real systems
The successful candidate will join the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) Centre for Doctoral Training (CDT), based at the University of Cambridge. The AI4ER CDT programme consists of a one-year Master of Research (MRes) course (two terms of formal teaching via lectures, practicals and team challenges plus a three-month research project), followed by a 3 year PhD project. Both the Masters and PhD research projects will be based on the above project description.
For further details on this project and how to apply please visit AI4ER’s applying to us webpages.