About The Role
Algorithms Engineer/Data Analyst – (KTP Associate).
This full‑time post is available from 1 September 2026 on a fixed term basis until 31 August 2028. The starting salary will start from £33,951 on Grade E, depending on knowledge, skills and experience.
The Role
Environment, Science and Economy
An exciting opportunity has become available to work on a 2‑year Knowledge Transfer Partnership (KTP) between the University of Exeter and QLM Technology Ltd. The KTP will develop novel AI‑enabled modelling capabilities to enable the quantification and location of methane emission sources from remote Lidar measurement.
The successful candidate will be employed by the University of Exeter and will spend the majority of their time working at QLM in Bristol, with occasional visits to the University of Exeter. The role requires full‑time commitment. It is a hands‑on role suited to someone curious about physical phenomena, passionate about environmental impact, and eager to solve complex real‑world problems using data.
Company Information
QLM Technology develops cutting‑edge quantum laser imaging solutions for greenhouse gas detection and monitoring. The company’s mission is to provide accurate, scalable, and cost‑effective tools to help industries measure, manage, and mitigate methane emissions—enabling a cleaner, more sustainable future. QLM is growing its data and algorithms capability and is seeking a motivated Algorithms Engineer / Data Analyst to join the team in Bristol.
About You
* Advanced degree (Masters minimum) in a technical subject, e.g. Mechanical Engineering, Physics, Applied Mathematics or equivalent relevant experience or a closely related field.
* Excellent communication skills (both written and oral) with ability to communicate complex and conceptual ideas to a range of people.
* Good communication skills, especially in explaining technical concepts to mixed audiences.
* Computational Fluid Dynamics and other computer‑aided design and analysis tools.
* Experience in machine learning, AI and neural networks.
* Familiar with Matlab, Python and Linux.
* Proficiency in Python, including data science libraries (NumPy, SciPy, Pandas, scikit‑learn, matplotlib/Plotly).
* Skilled in software programming for modelling, instrument control, data analysis, and processing.
* Strong analysis and problem‑solving skills.
* Strong analytical skills and ability to draw insight from complex real‑world datasets.
* Interest and ability to learn the underlying physics of gas emissions, atmospheric phenomena, and sensing systems.
* Ability to collaborate effectively within a multidisciplinary team environment.
* Capacity to become a strong project manager, taking on the leadership of a strategic innovation project.
* Understanding of scientific software development practices (version control, testing, documentation).
* Experience with spatial data, GIS tools, or atmospheric modelling is a plus.
* Exposure to laser‑based sensing, optics, or electronics beneficial but not essential.
Benefits
* A £4,000 training budget with 10% of your working time dedicated to personal development.
* A unique and challenging career opportunity, working with both industry and academia.
* For the right candidate there will be the opportunity to create an impactful and rewarding career with QLM after this project is complete.
* The successful applicant will develop a wide range of industrial and research skills for their future career, utilising the skills and knowledge of company supervisors, as well as benefitting from continuous academic support.
* The opportunity to manage and lead on a project early on in your career.
* Specific KTP residential training.
* The possibility to write academic papers and present at conferences.
* A chance to contribute directly to climate‑impacting technology.
* Work with a multidisciplinary team of physicists, software engineers, and field specialists.
* Learning and development opportunities in advanced sensing, data science, and environmental analytics.
* Hybrid working with a modern office environment in Bristol.
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