Research Associate in Atmospheric Dispersion Modelling (SENTINEL Project)

Independent; Leicester; Híbrido; GBP 37.000 - 45.000; Jornada completa

Descripción de la vacante

Independent seeks a Research Associate in Atmospheric Dispersion Modelling for the SENTINEL project in Leicester. You will lead physics-based dispersion modelling and validation, using NAME, CALPUFF, and ADMS to benchmark AI forecasts.

Prepare meteorological inputs, design hindcasting experiments, and assess plausibility of predictions for regulators and public health agencies. You will join an interdisciplinary School of Engineering team, collaborating with UK Health Security Agency and Met

Formación

• PhD or equivalent in atmospheric science or related field.
• Experience with physical modelling of environmental processes.
• Experience with atmospheric dispersion models (NAME, CALPUFF or ADMS).

Responsabilidades

• Lead physics-based dispersion modelling and validation for SENTINEL.
• Set up and run established atmospheric dispersion models (NAME, CALPUFF, ADMS).
• Prepare meteorological and geospatial inputs and design hindcasting experiments.
• Evaluate forecast performance against health guideline categories and odour reports.
• Collaborate with AI researchers and contribute to explainable AI analysis for regulators and public health agencies.

Conocimientos

Physical modelling

Analytical problem solving

Scientific communication

Python

R

Educación

PhD or equivalent

Herramientas

NAME model

CALPUFF

ADMS

Python

R

Descripción del empleo

Vacancy terms: Full time or job share, fixed term from 01 January 2027 to 31 March 2028

Hours per week:37.5

Advert closes midnight on:26 Nov 2026

About the role

As a Research Associate in Atmospheric Dispersion Modelling on the SENTINEL project, you will lead its physics-based dispersion modelling and validation work, helping to develop a new approach to forecasting chemical fugitive emissions. You will set up and run established atmospheric dispersion models, including ADMS, the Met Office NAME model and CALPUFF, to provide scientific benchmarks for SENTINEL's AI forecasts. You will prepare meteorological and geospatial inputs, design hindcasting experiments and evaluate forecast performance against health guideline categories and community odour reports. Working closely with the project's AI Research Associate, you will also assess whether predictions are physically plausible and contribute to explainable AI analysis and guidance for regulators and public health agencies.

You will join an interdisciplinary team in the School of Engineering, working across atmospheric science, AI, chemical fate and public health and collaborating with partners including the UK Health Security Agency and the Met Office. The project also has links with the University of Leicester's Institute for Environmental Futures, which brings together interdisciplinary research addressing environmental challenges and the relationships between environment and human health. This is an opportunity to contribute to high-quality research with practical applications, disseminate findings through publications and conferences, develop collaborations and help shape research at the intersection of atmospheric modelling, AI and public health.

About you

You will have a PhD, equivalent professional qualification or equivalent experience in atmospheric science, meteorology, environmental science or engineering, physics, applied mathematics or a related discipline. You will bring experience or demonstrable ability in the physical modelling of environmental processes, together with strong analytical problem-solving skills and evidence of contribution to peer-reviewed publications or equivalent technical outputs. Experience of an atmospheric dispersion model such as NAME, CALPUFF or ADMS is important, although candidates with a strong physics-based or numerical environmental modelling background and the ability to become proficient with these tools quickly are also encouraged to apply.

You will be able to use Python and/or R for scientific data processing, analysis and visualisation, and communicate complex information effectively to a range of audiences. You will be comfortable working both independently and collaboratively, including with internal and external stakeholders, and will bring a commitment to developing your expertise and contributing positively to a collegiate research environment. Experience with weather or geospatial data, AI or machine learning, high-performance computing, air-quality guidance, or hands-on use of NAME, CALPUFF or ADMS would be advantageous.

£40,704 per annum, pro-rata if part-time. Grade 7