PhD Studentship – Machine Learning: Explore the cutting-edge field of Quantum Technology Engineering with the University of Southampton’s PhD Studentship in Efficient End-to-End Quantum Machine Learning Strategies for Imaging. This opportunity, led by Prof Thomas Blumensath, offers substantial training in scientific, technical, and commercial skills alongside a research project focused on advancing quantum machine learning applications.
PhD Studentship – Efficient End-to-End Quantum Machine Learning Strategies for Imaging
Summary Table:
| Qualification Type | PhD |
|---|---|
| Location | Southampton |
| Funding | UK Students, EU Students, International Students |
| Funding Amount | Awarded on a rolling basis |
| Hours | Full Time |
| Placed On | 13th February 2024 |
| Closes | 31st August 2024 |
Study Area: This project delves into efficient quantum machine learning strategies, exploring the interaction of classical dimensionality reduction methods with quantum encoding and efficient quantum machine learning. The focus is on computational imaging, specifically in tomographic imaging, addressing challenges in large, three-dimensional data sets. The research aims to develop more efficient methods for tasks such as image classification, anomaly detection, and image de-noising.
Scholarship Description: The project spans the entire computational chain, from classical dimensionality reduction to quantum encoding and statistical decoding. The goal is to adaptively map an image into a lower-dimensional quantum state, enabling the development of specialized quantum algorithms for various image-related tasks. The research allows for the exploration of theoretical mathematical concepts, algorithm development, and applied computational experiments.
Eligibility:
- A very good undergraduate degree (at least a UK 2:1 honours degree or its international equivalent).
Required Documents:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
How to Apply: Apply online by searching for a Postgraduate Programme of Study on the University of Southampton website. Select program type as Research, Faculty of Engineering and Physical Sciences, and then choose “PhD iMR.” In Section 2 of the application form, insert the name of the supervisor (Prof Thomas Blumensath).
Applications should be sent to: feps-pgr-apply@soton.ac.uk.
Last Date: The closing date for applications is 31st August 2024. Applications will be considered in the order they are received, and the position will be deemed filled when a suitable candidate is identified.
Explore the future of quantum machine learning and imaging with this exciting PhD opportunity. For more information, contact Prof Thomas Blumensath at thomas.blumensath@soton.ac.uk. Visit the PhD Scholarships page for details on funding opportunities. Apply early to maximize your chance of consideration.
