Embark on a journey into the cutting-edge field of Quantum Technology Engineering with the University of Southampton PhD Studentship in Efficient End-to-End Quantum Machine Learning Strategies for Imaging. Led by Prof Thomas Blumensath, this opportunity provides comprehensive training in scientific, technical, and commercial skills, coupled with a research project focused on advancing quantum machine learning applications.
The project centers on exploring efficient quantum machine learning strategies, investigating the interplay between classical dimensionality reduction methods and quantum encoding, and developing efficient quantum machine learning techniques. Specifically, the research targets computational imaging, particularly in tomographic imaging, to tackle challenges in handling large, three-dimensional datasets. The objective is to devise more effective methods for tasks such as image classification, anomaly detection, and image de-noising.
Eligibility: Candidates should hold a very good undergraduate degree (at least a UK 2:1 honours degree or its international equivalent).
Documents Required:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
How to Apply: Interested candidates should apply online by searching for a Postgraduate Programme of Study on the University of Southampton website. Select the 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.
Join us at the University of Southampton and delve into the fascinating realm of Quantum Machine Learning. Apply now to be part of groundbreaking research in efficient quantum machine learning strategies for imaging.
For more detailed information about the application process, click here
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