Airborne Intelligent Monitoring and Quantification of Greenhouse Gases (Ref: AAE-CL-2521)
About the Project
Methane (CH₄) is the second most significant greenhouse gas after carbon dioxide (CO₂), with over 30 times the global warming potential over a 100-year period. It plays a key role in near-term climate action due to its short atmospheric lifetime and its prevalence in waste, energy, and agricultural sectors. Accurate, site-specific monitoring and quantification of CH₄ emissions are urgently needed to support the UK’s Net Zero strategy and global methane reduction commitments.
This PhD project aims to develop a next-generation airborne environmental monitoring system using unmanned aerial vehicles (UAVs) equipped with miniaturised gas sensors. The project will integrate artificial intelligence, active sensing, and robotic autonomy to enable UAVs to identify, sample, and quantify GHG emissions in real-time with high spatial and temporal resolution.
You will investigate how information-theoretic planning and active inference techniques can guide UAVs to collect the most valuable data during survey missions. The project will develop algorithms for real-time flight path adaptation, cooperative sensing among multiple UAVs, and the use of dispersion models and statistical inference to locate unknown sources and estimate their emission strength. Working with project partners and supported by LUCAS’s dedicated UAV test field, you will deploy and validate your system in realworld environments such as landfills, farms, or energy production sites.
Name of primary supervisor/CDT lead:
Cunjia Liu c.liu5@lboro.ac.uk
https://www.lboro.ac.uk/departments/aae/people/cunjia-liu/
Entry requirements:
We welcome applicants with a strong academic background in engineering, computer science, robotics, environmental science, or a related field. Skills in programming (e.g., Python, MATLAB, or C++), machine learning, or control systems are highly desirable. Prior experience with UAVs, sensing technologies, or fieldwork is a bonus but not essential.
A minimum of an upper-class honours degree (2:1) or overseas equivalent in a STEM subject area, or equivalent relevant industrial experience. Working knowledge of machine learning is desirable but not mandatory.
You should be highly motivated, enjoy problem-solving, and be keen to work across disciplines at the intersection of AI, robotics, and environmental sustainability.
English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).
Bench fees required: No
Closing date of advert: 30th September 2026
Start date: October 2026
Full-time/part-time availability: Full-time 3 years
Fee band: 2025/26 Band RB (UK £5,006, International £28,600)
How to apply:
- Stage 1: You are strongly advised to contact Prof Cunjia Liu in the first instance on c.liu5@lboro.ac.uk with a CV, academic transcripts, a reference letter, and confirmation of funding source. Informal discussions are also welcome.
- Stage 2: Following discussion with Prof Cunjia Liu, applicants will be invited to make a formal application at online. Under programme name, select Department of Aeronautical and Automotive Engineering and quote the advert reference number AAE-CL-2521 in your application.
Project search terms:
actuarial science, environmental sciences, robotics
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