At a Glance
- Tasks: Explore quantum machine learning strategies for imaging and develop innovative algorithms.
- Company: University of Southampton, a leader in Quantum Technology Engineering.
- Benefits: Substantial training, funding opportunities, and a chance to shape your research focus.
- Other info: Flexible project scope with excellent career development opportunities.
- Why this job: Dive into cutting-edge quantum tech and make a real impact in computational imaging.
- Qualifications: Strong undergraduate degree and passion for quantum computing and machine learning.
The predicted salary is between 18000 - 25000 £ per year.
The University of Southampton is expanding its PhD research in the area of Quantum Technology Engineering. In addition to the research project outlined below, you will receive substantial training in scientific, technical, and commercial skills.
This project looks at efficient quantum machine learning strategies, where near term quantum computers promise significant computational gains in a range of machine learning applications. Whilst advantages due to quantum parallelism and entanglement have been shown in a range of applications, efficient encoding of classical information into an entangled quantum state is known to be computationally challenging in general.
In this project, you will study and explore the entire computational chain, exploring the interaction of classical dimensionality reduction methods with subsequent quantum encoding strategies, coupled with efficient quantum machine learning performed on the low-dimensional, encoded state. Depending on the task, this will be coupled to a novel statistical decoding stage that will convert quantum measurements back into classical image information.
This project is inspired by problems in computational imaging and in particular in tomographic imaging, where we typically deal with large, three dimensional data. Due to the data size, using traditional machine learning approaches remains extremely challenging. Methods that can effectively exploit quantum parallelism would thus allow the development of more efficient methods for common problems such as image classification, anomaly detection and image de-noising.
Using theoretical algorithm development and simulated quantum computational experiments, in this PhD you will explore quantum computing strategies that use traditional computational methods to adaptively map an image into a lower dimensional representation. This representation can then be efficiently encoded as a quantum state. Starting with image classification and working towards more complex problems, specialised quantum computational algorithms can then be developed to efficiently operate on the embedded data.
This project has scope for the student to pursue their own interest and offers the opportunity to direct the focus on theoretical mathematical concepts, theoretical algorithm development and applied computational experiments.
If you are interested, please contact the supervisor for more information: Thomas Blumensath thomas.blumensath@soton.ac.uk
Entry RequirementsA very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date:31 August 2024. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.
Funding:We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
How To ApplyApply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), Faculty of Engineering and Physical Sciences, next page select “PhD iMR”. In Section 2 of the application form you should insert the name of the supervisor.
Applications should include:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
For further information please contact: feps-pgr-apply@soton.ac.uk
PhD: End-to-End Quantum ML for Imaging in Southampton employer: University of Southampton
The University of Southampton is an exceptional employer, offering a vibrant research environment that fosters innovation in Quantum Technology Engineering. With a strong emphasis on professional development, employees benefit from extensive training in scientific and commercial skills, alongside opportunities for personal growth through engaging projects like the PhD in End-to-End Quantum ML for Imaging. Located in a dynamic academic setting, the university promotes a collaborative work culture that values creativity and encourages exploration of cutting-edge technologies.
StudySmarter Expert Advice🤫
We think this is how you could land PhD: End-to-End Quantum ML for Imaging in Southampton
✨Tip Number 1
Network like a pro! Reach out to current PhD students or faculty members at the University of Southampton. They can give you insider info about the programme and might even put in a good word for you.
✨Tip Number 2
Prepare for your interview by diving deep into quantum machine learning topics. Brush up on your knowledge of classical dimensionality reduction methods and be ready to discuss how they relate to quantum encoding strategies.
✨Tip Number 3
Show off your passion! When you get the chance to chat with the supervisor, Thomas Blumensath, make sure to express your enthusiasm for the project and share any relevant experiences or ideas you have.
✨Tip Number 4
Don’t forget to apply through our website! The sooner you submit your application, the better your chances are of landing that spot. Plus, it shows you're proactive and serious about this opportunity.
We think you need these skills to ace PhD: End-to-End Quantum ML for Imaging in Southampton
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights relevant experience and skills that align with the PhD project. We want to see how your background fits into the world of Quantum Technology Engineering, so don’t hold back on showcasing your achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about quantum machine learning and how your interests align with the project. We love seeing enthusiasm and a clear vision for your research direction.
Gather Strong References:Choose referees who know your work well and can speak to your abilities in research and problem-solving. A solid reference can make all the difference, so give them a heads-up about the project and what you’d like them to highlight!
Apply Early!:Don’t wait until the last minute to submit your application. We review applications on a rolling basis, so getting yours in early increases your chances of being considered. Head over to our website and get started today!
How to prepare for a job interview at University of Southampton
✨Know Your Quantum Stuff
Make sure you brush up on your quantum machine learning concepts and the specific challenges mentioned in the project. Being able to discuss how classical dimensionality reduction methods interact with quantum encoding strategies will show your depth of understanding and genuine interest.
✨Showcase Your Research Skills
Prepare to talk about any previous research or projects you've worked on, especially those related to computational imaging or machine learning. Highlight your problem-solving skills and how you've tackled complex data challenges in the past.
✨Ask Insightful Questions
Come prepared with questions that demonstrate your curiosity about the project and the field. Inquire about the potential applications of the research or the types of statistical decoding methods they envision. This shows you're not just interested in the position but also in contributing meaningfully.
✨Tailor Your Application Materials
Ensure your CV and references highlight relevant experiences and skills that align with the PhD project. Mention any specific coursework or projects that relate to quantum technology or machine learning, as this will make your application stand out.
