Research Fellow in Multimodal Physics-Informed Foundation Model for Air Quality Applications

University of Surrey; Guildford; On-site; GBP 45,000 - 60,000; Part time

Job summary

University of Surrey invites applications for a world-class AI researcher to lead the development of a physics-informed, multimodal Foundation Model for air quality. You will work at the intersection of deep learning and atmospheric science, collaborating with Surrey's PAI, CVSSP and GCARE teams.

You will design, pre-train, and fine-tune large models on HPC resources, embedding physical constraints to ensure interpretability and policy relevance, while delivering web-based proof-of-concept tools

Qualifications

• Experience in designing and training large-scale DL architectures (e.g., Transformers, similar spatiotemporal models).
• Proven experience in multimodal data fusion (time-series, imagery, tabular).
• Strong proficiency in PyTorch and TensorFlow; experience deploying models on HPC clusters.
• Experience building web applications to operationalise models for policy decisions.
• Solid mathematical foundation in calculus and linear algebra; implement physics-informed constraints in NN losses.
• Track record of publishing high-quality research in AI or environmental science venues.

Responsibilities

• Architect a future-facing FM for air quality prediction fusing satellite, surface sensors, meteorological data.
• Embed physics constraints into model architecture for interpretability and trust.
• Apply super-resolution to translate coarse data into street-level insights.
• Fine-tune the pretrained FM for downstream applications and web-based proof-of-concept.
• Pretrain and fine-tune on Surrey's HPC infrastructure.
• Collaborate with national and international partners across academia and industry.

Skills

DL architectures

Multimodal fusion

PyTorch

TensorFlow

HPC deployment

Web apps

Math foundations

Research publishing

Physics-informed constraints

Tools

NVIDIA GPUs

Job description

Organisation/Company UNIVERSITY OF SURREY Research Field Computer science Physics Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Application Deadline 5 Nov 2026 - 00:00 (UTC) Country United Kingdom Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research.

We have a bold vision of what we want to achieve - shaping ourselves into one of the best universities in the world, which we are achieving through the talents and endeavour of every employee.

Our culture empowers people to achieve this aim and to make a real difference.

The role

Do you want to build the first-ever Foundation Model for air quality? We are looking for a world-class AI researcher to join the FMAI4Air project, a high-stakes mission to revolutionise how we predict air pollution, evaluating its impacts on people’s health and protecting millions of lives.

This isn't just another incremental AI project; we are pioneering a Physics-Informed Multimodal High-Resolution Foundation Model. You will be at the intersection of deep learning and atmospheric sciences, working within the prestigious and word-leading ecosystem of Surrey's PAI, CVSSP and GCARE, under the leadership of Assoc Prof Erick Sperandio.

Your role will be:

• Architect the Future of AI for Environmental Sciences: Design and pre-train a novel, task-agnostic, multimodal, high-resolution and physics-informed Foundation Model (FM) for air quality prediction that fuses heterogeneous data, from satellite imagery and surface sensors to meteorological grids and emissions inventories, and evaluates the impacts of air pollution on people’s health.
• Bridge AI and Atmospheric Physics: Move beyond the "black box" by embedding physical constraints (such as advection-diffusion equations) directly into the model architecture to ensure scientific consistency, interpretability and trustworthiness.
• Push the Resolution Limit: Implement state-of-the-art super-resolution and downscaling techniques to translate coarse global data into street-level, actionable insights.
• Solve Real-World Grand Challenges: Fine-tune the pretrained FM for critical downstream applications, including early-warning systems for wildfires and simulating urban air-quality mitigation scenarios to inform government policy through a web-based application for FMAI4Air proof-of-concept.
• Leverage High-Performance Computing: Pretrain and fine-tune the models on world-leading HPC infrastructure, including Surrey’s AI@Surrey and the national Isambard-AI HPC.
• Collaborate Nationally and Globally: You will work alongside a powerhouse consortium including Imperial College London, the University of Manchester, and NTNU (Norway), as well as with consortium partners from academia including IAG-USP (Brazil), NC Climate Office at NCSU (USA), Chiba University (Japan), from industry including NVIDIA and WSP Global, and from government such as Guildford Borough Council.

About you

• Expertise in designing and training large-scale Deep Learning architectures (e.g., Transformers, Mamba, or similar spatiotemporal models).
• Proven experience in multimodal data fusion (handling a mix of time-series, imagery, and tabular data).
• Strong proficiency in PyTorch and Tensorflow, and experience deploying models on HPC clusters and on the edge for fast and efficient inference.
• Expertise in developing and deploying Web applications that will be used to operationalise FMAI4Air models for policy and decision making.
• A solid mathematical foundation in calculus and linear algebra, with the ability to implement physics-informed constraints into neural networks’ loss functions.
• A track record of publishing high-quality research in top-tier AI or environmental science conferences/journals.
• Commitment to deliver high-quality research and innovation outcomes, including publishing papers in top-tier venues, and support research, dissemination, communication and management activities.