TUO
Adaptive Multi-Objective Search in Expensive High-Dimensional Socio-Technical Systems
✓ Funded (Competition)
⏰ Closing Soon
🎓 Artificial Intelligence
🎓 Control Systems
🎓 Machine Learning
🎓 Operational Research
🎓 Stochastic Processes
AI CDT
Bayesian optimisation
EV networks
Gaussian processes
decision making
high-dimensional search
multi-objective optimisation
socio-technical systems
surrogate modelling
Industry-linked PhD at the University of Manchester with Honda Research Institute Europe, developing machine learning methods for high-dimensional multi-objective optimisation in complex systems like EV energy networks.
Project Description
This PhD project focuses on developing advanced machine learning techniques for solving optimisation problems in complex socio-technical systems, in collaboration with Honda Research Institute Europe.
Modern systems such as electric vehicle (EV) energy networks involve large-scale decision-making under multiple competing objectives, including efficiency, fairness, sustainability, and user satisfaction. These problems are computationally expensive and high-dimensional, making traditional optimisation approaches insufficient.
The project will explore cutting-edge approaches such as:
Bayesian optimisation for efficient search under limited evaluation budgets
Gaussian processes for surrogate modelling of expensive objective functions
Multi-objective optimisation to balance competing criteria
Adaptive variable selection for navigating high-dimensional spaces
Key research goals include:
Efficient exploration of large decision spaces using adaptive search strategies
Modelling trade-offs between conflicting objectives using multi-output models
Incorporating human-centred criteria such as trust, fairness, and explainability
Developing scalable optimisation methods for real-world industrial systems
The successful candidate will benefit from close collaboration with industry, including research visits and engagement with Honda’s international research community.
Entry Requirements
Strong background in machine learning, statistics, applied mathematics, or optimisation
Experience with probabilistic modelling or optimisation methods
Knowledge of Bayesian optimisation or Gaussian processes (desirable)
Strong programming and analytical skills
Experience with probabilistic modelling or optimisation methods
Knowledge of Bayesian optimisation or Gaussian processes (desirable)
Strong programming and analytical skills
How to Apply
Apply via the University of Manchester application portal under PhD in Artificial Intelligence (AI CDT)
Required documents:
CV
Academic transcripts and certificates
Supporting statement (1–2 pages)
Two referees
English language certificate (if applicable)
Ensure you:
Mention the project title
Include supervisor names
Submit all documents before the deadline
Required documents:
CV
Academic transcripts and certificates
Supporting statement (1–2 pages)
Two referees
English language certificate (if applicable)
Ensure you:
Mention the project title
Include supervisor names
Submit all documents before the deadline
Eligibility
UK/Home
EU
International
Supervisor Profile
RA
Richard Allmendinger, Ahmed Kheiri, Mauricio Alvarez
The University of Manchester, PhD in Artificial Intelligence (AI CDT – Decision Making for Complex Systems)
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