Overview
This is an exciting opportunity for a fully-funded PhD scholarship at the University of Manchester, focusing on the application of transfer learning to Bayesian optimization within the realm of computational fluid dynamics (CFD). This program is particularly significant as it aims to enhance the efficiency of data-driven methods in CFD, which are critical in various engineering and scientific applications.
Background & Relevance
Transfer learning and Bayesian optimization are pivotal in machine learning, especially for tasks that involve expensive simulations or experiments. In CFD, the ability to optimize processes and models efficiently can lead to significant advancements in research and industry. By leveraging existing models and adapting them to new conditions, researchers can save time and resources, making this field of study highly relevant to current challenges in AI and engineering.
Key Details
- Application Deadline: December 5, 2025
- Start Date: September 2026
- Location: University of Manchester, UK
- Scholarship Includes:
- Fully funded 4-year program
- Home tuition fees covered
- Competitive tax-free stipend of at least £20,780 per year
- Application Link: Apply Here
Eligibility & Participation
This scholarship is aimed at home students, which includes UK nationals, Irish nationals, EU nationals with indefinite leave to remain, and certain categories of residents in the UK. Candidates should have a strong background in machine learning or related fields, and a keen interest in applying these techniques to real-world problems in fluid dynamics.
Submission or Application Guidelines
To apply for this scholarship, candidates should follow these steps:
1. Visit the application link provided.
2. Complete the application form with relevant personal and academic information.
3. Submit any required documentation as specified on the application page.
4. For inquiries, contact the UKRI AI Decisions CDT Team at aidecisionscdt@manchester.ac.uk.
Additional Context / Real-World Relevance
The integration of transfer learning with Bayesian optimization represents a significant advancement in the field of computational fluid dynamics. As industries increasingly rely on data-driven decision-making, the ability to efficiently adapt existing models to new scenarios becomes crucial. This research not only contributes to academic knowledge but also has practical implications in engineering, environmental science, and beyond.
Conclusion
This fully-funded PhD scholarship presents a unique opportunity for aspiring researchers to delve into the innovative intersection of transfer learning and Bayesian optimization. Interested candidates are encouraged to apply and contribute to this vital area of research, which holds promise for advancing both theoretical and practical applications in AI and fluid dynamics.
Category: PhD & Postdoc Positions
Tags: transfer learning, bayesian optimization, computational fluid dynamics, machine learning, AI, University of Manchester, surrogate models, active learning
