Research Associate in AI for Environmental Forecasting (SENTINEL project)

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

Descripción de la vacante

University of Leicester invites a Research Associate to lead the AI development strand for the SENTINEL project, building models to forecast environmental emissions 24 to 72 hours ahead.

You will work with interdisciplinary teams, train and evaluate ML systems, and leverage GPU/HPC resources while contributing to peer-reviewed publications and future collaborations.

Formación

• PhD or equivalent qualification or experience in a quantitative field.
• Experience developing and training machine learning or deep learning models using real-world data.
• Strong Python programming skills and experience with modern AI tools (PyTorch, TensorFlow, JAX).
• Ability to work independently and in a collaborative research team; clear communication with stakeholders.

Responsabilidades

• Lead the AI development strand of SENTINEL, a UKRI-funded project.
• Build multi-modal training data 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.
• Utilise GPU/HPC resources and contribute to high-quality research outputs.

Conocimientos

Python programming

Machine learning

Data analysis

Communication

Educación

PhD or equivalent

Herramientas

PyTorch

TensorFlow

JAX

NumPy

pandas

xarray

Descripción del empleo

# Research Associate in AI for Environmental Forecasting (SENTINEL project)University of Leicester Leicester, United KingdomSalary: **GBP 40,704 per year**Application Deadline: **November 23, 2026**Position Type: **Researcher**Date Posted: **November 6, 2026**Contact Email: **[e-mail]**Source details may be limited## Project Description### About the roleAs 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 youYou 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 informationInformal enquiries are welcomed and should be directed to Dr Timothy Pearce at [e-mail] 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.Unable to show this video due to your cookie preferencesJob Summary Download 14072\\_Job\\_Summary.pdf---### Academic [e-mail]06/11/2026Last UpdateUniversity of Leicesterjobs.le.ac.ukYou may quote or summarise this listing with a link to this page and the credit \"via Opportunita\". AI training and automated bulk collection are not permitted. AI and data reuse policy## Apply for this positionThis application must be completed through the official university portal.Apply on official website