TUO
Advancing Fusion Energy with AI- Developing Novel Compressed Representations for High-Dimensional Physics Data
✓ Funded (Competition)
⏰ Closing Soon
🎓 Applied Statistics
🎓 Artificial Intelligence
🎓 Atomic Engineering
🎓 Data Science
🎓 Metallurgy
🎓 Thermodynamics
Dimensionality Reduction
Fusion Energy
Graph Neural Networks
High-Dimensional Data
Machine Learning
Materials Science
Predictive Modelling
Surrogate Modelling
Time-Series Analysis
Tokamak
Fusion Energy, Tokamak, Machine Learning, Graph Neural Networks, High-Dimensional Data, Surrogate Modelling, Materials Science, Predictive Modelling, Dimensionality Reduction, Time-Series Analysis
Project Description
Tokamak fusion reactors are a promising source of clean energy, but their structural materials operate under extreme conditions, including high temperatures and radiation. Radiation creates complex, evolving defect structures in the material, and predicting this damage is crucial for assessing component failure risk.
High-fidelity atomistic simulations generate massive datasets describing millions of atomic positions. This project treats the challenge as one of dimensionality reduction and feature extraction, developing compressed “fingerprints” of these complex atomic systems.
You will:
Design and develop novel descriptors, with a focus on graph network-based methods (GNNs).
Train and validate ML models on large, pre-existing datasets of damaged atomic structures.
Model latent space dynamics from time-series data, moving from static snapshots to dynamic, predictive models.
The outputs will feed into surrogate models for radiation damage, accelerating in-silico design and qualification of fusion reactor components. The work combines geometric deep learning, unsupervised representation learning, and time-series analysis.
Industry Collaboration:
This project includes an industry partner, UK Atomic Energy Authority (UKAEA), with an industry supervisor: Pratheek Shanthraj (pratheek.shanthraj@ukaea.uk)
Entry Requirements
Strong background in Mathematics, Computer Science, Data Science, or a related scientific domain
Experience or strong interest in machine learning, high-dimensional data, and predictive modelling
Experience or strong interest in machine learning, high-dimensional data, and predictive modelling
How to Apply
Apply via University of Manchester Application Portal under “PhD in Artificial Intelligence”
Required documents:
Final transcripts and certificates of all awarded qualifications
Interim transcripts of in-progress qualifications
CV
Supporting statement outlining motivation, relevant experience, and research skills
Contact details for two referees
English language certificate (if applicable)
Applications incomplete without all documents will not be processed
Required documents:
Final transcripts and certificates of all awarded qualifications
Interim transcripts of in-progress qualifications
CV
Supporting statement outlining motivation, relevant experience, and research skills
Contact details for two referees
English language certificate (if applicable)
Applications incomplete without all documents will not be processed
Eligibility
UK/Home
EU
International
Supervisor Profile
DJ
Dr Julia Handl, Prof J Robson
The University of Manchester, Department of Computer Science / AI CDT
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