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.

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.

Applications to the CDT will open in November, 2024 for the next cohort to start in September 2025

Admission to the CDT are 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.

• Application period: November 13, 2024 to January 31, 2025.
• Interviews: late March/early April, 2025.
• Decisions: late-April/early May, 2025.