Develop High-Accuracy Prediction Models with Small Datasets and Uncertainty of One-sided Information in the Free-Market System PhD

This PhD project offers a unique opportunity to delve into the complexities of free-market systems and sustainability through a novel ensemble prediction model. With a focus on addressing uncertainty and limited data availability, this research aims to revolutionize decision-making in End-of-Life product management. This initiative addresses crucial gaps in sustainable environmental management and economic resilience.

The project pertains to the field of informed decision-making in End-of-Life (EOL) product management, a critical area that focuses on effectively managing products once they reach the end of their lifecycle. As industries and governments seek to address sustainability challenges, this discipline is increasingly relevant today, particularly with the rising demand for sustainable waste management and resource efficiency in free-market economies.

The primary aim of this project is to develop a sophisticated ensemble prediction model specifically tailored to address the complexities of free-market systems. This model will support more accurate forecasting and decision-making for EOL product management, assisting stakeholders in navigating the uncertainties associated with recycling and resource recovery.

扣扣传媒 stands out as an ideal institution for this research due to its extensive resources and expertise in engineering and digital technologies. The university's strong focus on applied research and innovation makes it a perfect environment to undertake this project. In addition, the collaboration with industry partners and leading experts provides valuable insights and support for the development of the prediction model.

The expected impact of this research is far-reaching. The developed prediction model has the potential to revolutionise strategies within recycling programs, resource allocation, and waste management. By enhancing decision-making, it will foster more resilient economies and contribute to sustainable circular practices, ultimately reducing environmental impact and promoting economic growth through effective resource management.

One of the unique selling points of this project is the opportunity for collaboration with world-leading experts within the International Systems Realization Partnership. The student will also benefit from exposure to international conferences, travel opportunities, and external training that will enrich their understanding and contribute to the success of the project.

Through this experience, the student will gain invaluable transferable skills such as analytical thinking, problem-solving, and advanced data management. These skills will enhance their employability, positioning them as highly sought-after professionals in the fields of engineering and digital innovation.

At a glance

Application deadline06 Aug 2025
Award type(s)PhD
Start date29 Sep 2025
Duration of award3 years
EligibilityUK, EU, Rest of world
Reference numberSATM543

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit individuals from a variety of backgrounds such as machine learning, statistics, and economics. Candidates with experience or keen interest in predictive modelling, data analytics, sustainability studies, or any interdisciplinary fields that combine technology with economic and environmental considerations are particularly encouraged to apply. This opportunity is ideal for candidates who are looking to make a tangible impact on advancing predictive technologies and sustainable practices through rigorous academic research and practical application.

Funding

This is a self-funded project, any interested applicants or any potential sponsor (local government or industry) will need to provide their own financial support in relation to tuition fees, research support fees and living expenses.

This studentship is open to both UK and International applications.

Find out more about tuition fees.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.