Research Associate in AI for Environmental Forecasting (SENTINEL project)

University of Leicester; Leicester; Presencial; GBP 37.000 - 45.000; Jornada completa

Descripción de la vacante

University of Leicester invites applications for a Research Associate in AI for Environmental Forecasting (SENTINEL project) to lead the AI development strand of a UKRI-funded effort building a physics-informed AI forecasting system for chemical fugitive emissions.

You will design, train and evaluate the Receptor Transfer Function Learner, assemble multi-modal data, and work with GPU/HPC facilities while collaborating with UK Health Security Agency and Met Office.

Formación

• PhD or equivalent in a quantitative discipline with track record in ML/DL.
• Experience developing ML/DL models with real-world time-series data.
• Strong Python programming and experience with PyTorch, TensorFlow or JAX.
• Experience contributing to peer-reviewed publications or equivalent research outputs.
• Ability to work independently and in a collaborative research team; clear communication.
• Experience with graph neural networks, diffusion models, physics-informed ML, weather/geospatial data, HPC advantageous.

Responsabilidades

• Lead the AI development strand of SENTINEL project.
• Build a multi-modal training dataset and design, train and evaluate the Receptor Transfer Function Learner.
• Benchmark SENTINEL against established dispersion models and publish results.
• Collaborate with partners including UK Health Security Agency and Met Office.
• Contribute to high-quality research publications and dissemination.
• Utilise GPU clusters or HPC resources for experiments.

Conocimientos

Graph neural networks

Generative models

Diffusion models

Physics-informed ML

Explainable AI

Weather data

Geospatial data

GPU / HPC experience

Educación

PhD or equivalent in a quantitative discipline

Herramientas

Python

PyTorch

TensorFlow

JAX

NumPy

pandas

xarray

Descripción del empleo

Vacancy terms

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

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.

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