Research Associate in AI for Environmental Forecasting (SENTINEL project)

Job description

About the role

As Research Associate in AI for Environmental Forecasting (SENTINEL project), you will lead the AI development strand of SENTINEL, a UKRI-funded project developing a physics-informed AI forecasting system for chemical fugitive emissions. You will build a multi-modal training dataset and design, train and evaluate the Receptor Transfer Function Learner, combining generative AI downscaling, graph neural networks and explainable AI to forecast community exposure to fugitive emissions 24 to 72 hours ahead. You will work with real-world environmental, meteorological, operational and community data, applying rigorous, reproducible research methods and using GPU and high-performance computing facilities.
Working within the School of Engineering, you will collaborate with an interdisciplinary team spanning AI, atmospheric dispersion, chemical fate and public health, as well as project partners including the UK Health Security Agency and the Met Office. You will benchmark SENTINEL against established dispersion models, contribute to high-quality research publications and dissemination, and help develop collaborations and future research opportunities. The role sits within a research environment where AI is being applied to complex datasets and global challenges, including environmental monitoring and sustainability.

About you

You will have a PhD, equivalent professional qualification or equivalent experience in computer science, machine learning, data science, environmental data science, physics, engineering or another related quantitative discipline. You will bring demonstrable experience of developing and training machine learning or deep learning models using real-world, noisy or time-series data, alongside strong analytical problem-solving skills and experience contributing to peer-reviewed publications or equivalent research outputs. You will have strong Python programming skills, including experience with modern AI or deep learning tools such as PyTorch, TensorFlow or JAX and scientific Python tools such as NumPy, pandas or xarray.
You will be able to work effectively both independently and as part of a collaborative research team, communicate complex information clearly and engage confidently with internal and external stakeholders. Experience with graph neural networks, generative or diffusion models, physics-informed machine learning, explainable AI, weather or geospatial data, atmospheric dispersion modelling, or training models using GPU clusters or HPC would be advantageous. You will join a research environment in which high-performance AI, physics-informed approaches and digital twins are being developed for applications including environment, health and engineering.

Additional information

Informal enquiries are welcomed and should be directed to Dr Timothy Pearce at [email protected]

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 found here.

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.

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