曼彻斯特大学（英国）PhD position in Compressed descriptors of damage microstructures in fusion materials申请条件要求-申请方
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曼彻斯特大学（英国）
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About the Project
AI_CDT_DecisionMaking
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Structural materials in a tokamak fusion reactor are designed to withstand extreme temperatures, stresses, and radiation damage during their operation. Radiation damage, from bombardment with high-energy neutrons, result in complex defect structures that evolve across a wide range of time and length scales. Quantifying this damage structure and how it relates to degradation of material strength is critical to assessing risk of failure in reactor components.
While experimental characterization under neutron irradiation is time consuming, expensive and, in many cases, impossible, atomistic simulation methods can model individual damage events, involving millions of atoms, with a high level of fidelity to the true physics. However, raw data from these simulations, described by the positions of millions of atoms, is too large to parameterize predictive models of long-term damage accumulation across reactor components. Thus, the key to unlocking reduced order models for radiation damage lies in the development of a compressed representation of the underlying defect structures. Such a microstructure fingerprint reduces model parameterization from millions of atoms to a more manageable number of descriptor features.
Machine learning methods are increasingly accelerating the development of these fingerprints by treating microstructure evolution as a pattern recognition problem [1]. In this project we would like to explore algorithms for the compressed description of long-range atomic neighborhoods that will provide a framework for parsimoniously fingerprinting radiation damage structures. Recent work on developing local atomic descriptors in the context of fitting potential energy surfaces [2,3] could serve as a starting point, but the potential of alternative, e.g. graph network-based, methods would also be interesting to explore. We will have access to a large database of damaged atomic structures to train, validate, test and compare various compression methods. Such compressed descriptors are crucial to enable reduced order models for structure-property predictions in damaged materials. Furthermore, time-series data will also be available to learn the latent space dynamics of these damage descriptors.
The outcome of this project will enable the parameterization of fast and actionable reduced order models for radiation damage evolution in fusion reactor components, which will significantly accelerate their in-silico design and qualification.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. (Equality, diversity and inclusion | The University of Manchester)( https://www.findaphd.com/common/clickCount.aspx?theid=177052&type=184&DID=7745&url=https%3a%2f%2fwww.manchester.ac.uk%2fconnect%2fjobs%2fequality-diversity-inclusion%2f )
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.
曼彻斯特大学（英国） PhD position in Compressed descriptors of damage microstructures in fusion materials项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
This is a fully funded AI UKRI CDT 4 year program; Home tuition fees will be provided, along with a tax-free stipend (subject to individual circumstances), set at the UKRI rate (e.g. £19,237 for 2024/25) . The start date is September 2025.
Funded project with UK Atomic Energy Authority - Industry Partnership based in University of Manchester
Company website: UK Atomic Energy Authority - GOV.UK( https://www.findaphd.com/common/clickCount.aspx?theid=177052&type=184&DID=7745&url=https%3a%2f%2fwww.gov.uk%2fgovernment%2forganisations%2fuk-atomic-energy-authority )
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