CLiMET: CFD-LiDAR Methane Estimation

Methane is a potent greenhouse gas with a global warming potential significantly greater than carbon dioxide over short time horizons. Accurate quantification of methane emissions from diffuse and complex sources—such as energy infrastructure, landfills, and urban environments—is a major scientific and regulatory challenge. This PhD project seeks to develop an integrated framework combining computational fluid dynamics (CFD) with quantum gas LiDAR measurements to improve the quantification of methane emissions. The project involves three core objectives: developing high-fidelity CFD models of methane dispersion in representative environments; integrating CFD predictions with quantum gas LiDAR data using simulated and experimental measurements; and quantifying uncertainty in inferred emission rates from flow variability, measurement noise, and model assumptions. Methodologically, CFD simulations, surrogate modeling, and inverse techniques will be combined, supported by high-performance computing. A novel aspect is using LiDAR perception pipelines to automate CFD domain generation directly from raw measurement data. Validated CFD models tailored for LiDAR-based methane sensing. A coupled modeling–measurement framework for methane emission rate inference. Quantitative uncertainty assessment in methane emission estimates. This research addresses critical challenges in environmental monitoring by advancing methane emissions quantification. It will improve methane emission inventories and support mitigation strategies, contributing valuable tools at the convergence of fluid dynamics, atmospheric sensing, and environmental science.

Bachelor's and postgraduate degree in Biomedical Engineering, Electrical/Electronic Engineering, Computer Science, or a related field. Experience in Python/MATLAB, signal processing, or machine learning. Strong interest in AI for healthcare and wearable technologies. Strong analytical, programming, and communication skills. Demonstrated capability in delivering research projects at undergraduate or postgraduate level. Non-native English speakers must provide evidence of English language competency per SETU requirements. Desirable: Master's degree in AI, biomedical engineering, or a related discipline; experience in machine learning, signal processing, or healthcare data; interest in wearable technologies and digital health; prior experience in scientific paper and report writing.
How to apply:
Complete the online application form via the SETU website, quoting reference code SETU_2025_06_WSCH. Ensure all supporting documents are uploaded as part of the submission. Applications must be submitted through this route only. For project queries: bhaskar.murari@setu.ie. For application and admissions queries: researchadmissions@setu.ie or +353 (0)51 302883. Closing date: 22 April 2026 at 4pm Irish time.

For further information and to apply, please visit: https://www.exeter.ac.uk/study/funding/award/?id=5847