About

Vision and scope

The CDT will enable students to develop new fundamental AI capabilities in the context of a diversity of complex systems. Rather than working in isolation, as is usual in AI, the students will learn to develop these in a collaborative manner tied to a specific application domain. In particular, the CDT will comprise three topics:

Uncertainty in complex systems (UQ). Human and AI collaborative decision making requires principled uncertainty quantification. The CDT will develop leading-edge probabilistic machine learning methods, leveraging uncertainty in a statistical manner to drive the exploration of new parameter spaces and promote scientific discovery. With a focus on the methodological and theoretical aspects, the CDT will provide impact across any field where decision-making is critical. Uncertainty quantification and modelling will underpin the following two topics of the CDT:

Decision-making with humans in the loop (DMHL). By acknowledging that the human user, in many cases, is unable to fully specify the details computer systems require, and by jointly modelling the machine learning task and the user, AI technologies will be more efficient in addressing key challenges such as experimental design from scarce data and domain shift, as well as promoting trust in AI-enabled systems.

Decision-making for ML systems (DMML). Increasingly, several automated decision-making systems are being built by a composition of individual machine learning components making decisions at the component level. For scientific systems that produce huge data volumes, so-called “big science” (e.g., SKA, CERN), AI-driven decisions are increasingly necessary to replace human decisions at multiple points within scientific analyses and facility operations. The CDT will look at automated AI approaches that can ensure such systems combination is robust, safe and accurate.

Model interpretability and explainability will be transversal to the three topics above. Decision making with AI needs to be interpretable and explainable to facilitate interrogation of decision processes such that trust can be built by the human, and it is essential for understanding and meeting ethical and legal implications.

Primary Research Area: Science and Research.

We will start with three different types of fields of research, each allowing the cohort an opportunity to explore different kinds of new questions and identify different kinds of hypotheses with AI: Physics/Astronomy, Engineering Biology and Material Science. In Physics/Astronomy, we will work closely with collaborators at the University of Manchester (UoM) and the University of Cambridge (UoC) to bring together simulation science and ML/AI methods. The intersection of simulation science and ML/AI will drive progress in scientific experimentation and discovery and has often been driven by the “big physics” community. More widely, AI solutions must work for real-world scientific operations such as the new generation of astronomy and physics facilities, including the Square Kilometre Array and Large Hadron Collider, meaning the solutions provided must also be scalable. For Engineering Biology (EB), we envision a close collaboration with the Manchester Institute of Biotechnology in projects related broadly to human-interpretable, multi-scale predictive models that can direct the automated design and optimisation of biological systems. The ML/AI tools and methods developed through the CDT can be applied to virtually any EB application area with envisioned impacts in strategic domains of National importance: Clean Growth, Sustainable Agriculture and Bioproduction, Biomedicine, Environmental Solutions, and Novel Materials. In Material Science (MS), we will work with the Henry Royce Institute and the UK Atomic Energy Authority in ML/AI aspects of digital engineering for Materials 4.0. Among others, Royce will provide materials data repositories, and we will jointly develop physics-based modelling powered by ML/AI to accelerate materials innovation and manufacturing.

Cross-cutting theme: AI for increasing business productivity.

As part of the co-creation process for this CDT, we had conversations with different companies and organisations interested in the three topics that the CDT addresses, UQ, DMHL, and DMML. We will work closely with companies to ensure that the methods developed in the science domain are adapted to various business settings, ensuring real-world practical impact. An important prerequisite for this is to develop methodologies that are reproducible and accessible to all research, innovation and industry communities. To support and steer the CDT in carrying out research that translates into increasing business productivity and ultimately contributes to increased living standards and well-being, we will be working with The Productivity Institute (TPI). TPI is a UK-wide research organisation headquartered at AMBS, UoM, with UoC being one of eight partners, each partner leading a regional Productivity Forum. It includes representatives from policy, community and industry.

CDT

The UKRI AI Center for Doctoral Training (CDT) in Decision Making for Complex Systems is a joint CDT between the University of Manchester and the University of Cambridge. The CDT provides funding for four years of advanced studies towards a PhD. The first year is a taught program that will cover the fundamentals of Machine Learning. This year is followed by three years of research at either at Manchester and/or Cambridge.

Projects

Compressed descriptors of damage microstructures in fusion materials

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 characterisation 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. In this project, we propose to generalize recent advances in microstructure image fingerprinting to graph data, which is the more natural setting to represent changes in local atomic neighbourhoods. Graph-based autoencoders will be developed to extract global and local descriptors of radiation damage microstructures in Tungsten – a critical plasma facing component which is exposed to the highest neutron doses in a reactor – using a large dataset of high-fidelity atomistic damage simulations. Furthermore, time-series data will also be used to learn the latent space dynamics of these damage descriptors. The outcome of this project will enable the parameterisation 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.

Admission

Admission to the CDT will be made on a project-by-project basis.

Applicants must apply through the official university’s website, create an account and select the PhD Artificial Intelligence CDT program.

When asked about “Research details”, applicants can use the project details they are applying to.

When asked about “Funding sources”, applicants can choose the option “Research council”; most of the other fields in this section are not mandatory.

Regarding the Supporting Statement, it is a one or two page statement outlining your motivation to pursue postgraduate research, the area(s) of research you’re interested in, why you want to undertake postgraduate research at Manchester/Cambridge, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed.

First round of admissions (key dates):

  • Application deadline: March 31, 2024.
  • Interviews: late April, early May, 2024.
  • Decisions: late May, early June, 2024.

Second round of admissions (key dates):

This second round will only accept home applicants

  • Application deadline: June 17, 2024.
  • Interviews: late June, early July, 2024.
  • Decisions: mid July, 2024.

People

Supervisor

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Matthew Colbrook

Assistant Professor in Machine Learning

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Anirbit Mukherjee

Lecturer in Machine Learning

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Silvino Fernandez Alzueta

Senior Scientist - Mathematical Optimization

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Caterina Doglioni

Professor of Particle Physics

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Michael Hobson

Professor of Astrophysics

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Will Handley

Royal Society University Research Fellow

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Patrick Cai

Professor of Synthetic Genomics

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Julia Handl

Professor Decision Sciences

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Manuel López-Ibáñez

Senior Lecturer

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Alejandro Frangi

Professor of Computational Medicine

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Carl Rasmussen

Professor of Machine Learning

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Christopher Conselice

Professor of Astrophysics

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Christopher Race

UKAEA Chair in Fusion Materials

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Eriko Takano

Professor of Synthetic Biology, Chemical Biology and Biological Chemistry

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Frank Bretz

Distinguished Quantitative Research Scientist

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Gavin Brown

Professor of Machine Learning

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Hongpeng Zhou

Dame Kathleen Ollerenshaw Fellow

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Joseph Robson

Professor of Physical Metallurgy

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Kostas Sechidis

Associate Director of Data Science

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Mateja Jamnik

Professor of Artificial Intelligence

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Mauricio A Álvarez

Senior Lecturer in Machine Learning

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Michele Caprio

Lecturer in Machine Learning

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Omar Rivasplata

Incoming Senior Lecturer in Machine Learning

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Pratheek Shanthraj

Principal Scientist at UKAEA

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Rainer Breitling

Professor of Systems Biology, Chemical Biology and Biological Chemistry

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Richard Allmendinger

Professor of Applied AI

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Roxanne Guenette

Professor of Particle Physics

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Samuel Kaski

Professor of AI

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Stanislas Pamela

Head of the AI & Machine Learning group, UKAEA

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Wei Pan

Senior Lecturer in Machine Learning

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Anna Scaife

Professor of Radio Astronomy

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Carl Henrik Ek

Associate Professor Machine Learning

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Henry Moss

Early Career Advanced Fellow

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Magnus Rattray

Professor of Computational and Systems Biology

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Mingfei Sun

Lecturer in Machine Learning

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Sebastian Ober

Senior Scientist for ML

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Susan Astley

Professor of Intelligent Medical Imaging

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Timothy Nunn

Research Software Engineer

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Tom Diethe

Head of The Centre for Artifical Intelligence

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Vignesh Gopakumar

Scientific Machine Learning Engineer