Research Associate in Atmospheric Dispersion Modelling (SENTINEL Project)

University of Leicester; Leicester; Híbrido; GBP 34.000 - 42.000; Jornada completa

Descripción de la vacante

University of Leicester is seeking a Research Associate in Atmospheric Dispersion Modelling for the SENTINEL project. You will lead physics-based dispersion modelling, run established models, and prepare inputs to benchmark AI forecasts.

You will collaborate across atmospheric science, AI, chemical fate and public health, contributing to publications and policy-relevant guidance. A PhD or equivalent is required, with strong modelling and coding skills.

Formación

• PhD or equivalent in atmospheric/environmental science, engineering or related field.
• Experience with physical modelling of environmental processes.
• Experience with NAME, CALPUFF or ADMS is desirable.
• Proficiency in Python and/or R for data processing, analysis and visualization.
• Ability to work independently and in teams with stakeholders.
• Experience with weather/ geospatial data; AI or machine learning is advantageous.

Responsabilidades

• Lead physics-based dispersion modelling and validation for SENTINEL.
• Set up and run 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; contribute to explainable AI analyses for regulators.

Conocimientos

Atmospheric science

Meteorology

Environmental science

Engineering

Physics

Applied mathematics

Python

R

High-performance computing

AI/ML

Educación

PhD (or equivalent) in a relevant field

Herramientas

NAME model

CALPUFF

ADMS

Descripción del empleo

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.

Additional information

Informal enquiries are welcomed and should be directed toDr Timothy Pearce at [e-mail]

As part of the University’s ongoing commitment to professional development, this role will also be considered on a seconded basis for existing staff members. Please ensure this is discussed with your line manager prior to applying. More information regarding secondments can be foundhere.

The University of Leicester has been changing the world, and changing people’s lives, for 100 years. When you join us, you’ll become part of a community of Citizens of Change, which includes not only our staff and our current students but also thousands of Leicester graduates around the world.

As a diverse and forward-thinking employer, we embed the principles of equity, diversity and inclusion into everything we do. That includes not just our core missions of teaching and research, but also our support for staff, students and our local community through our values of Inspiring, Impactful and Inclusive through our values of Inspiring, Impactful and Inclusive.

We’re committed to the wellbeing of all our staff and to the sustainability of our environment, on our campus and beyond. We offer a competitive salary package, excellent pension scheme and a generous annual leave allowance, along with opportunities to develop your career in a supportive and collaborative environment.

Vacancy terms

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