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: Receive extensive training, funding opportunities, and a chance to shape your research focus.
- Other info: Applications are reviewed on a rolling basis; apply early for the best chance!
- Why this job: Dive into cutting-edge quantum tech and tackle real-world imaging challenges.
- Qualifications: Must have a strong undergraduate degree (UK 2:1 or equivalent).
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 Studentship: Efficient End-to-End Quantum Machine Learning Strategies for Imaging in Southampton employer: University of Southampton
The University of Southampton is an exceptional employer for aspiring researchers, offering a vibrant work culture that fosters innovation and collaboration in the cutting-edge field of Quantum Technology Engineering. With substantial training in scientific, technical, and commercial skills, employees benefit from a supportive environment that encourages personal and professional growth, alongside access to a range of funding opportunities for both UK and international students. Located in a dynamic academic setting, this PhD studentship provides a unique chance to engage with pioneering research while contributing to advancements in quantum machine learning strategies for imaging.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Efficient End-to-End Quantum Machine Learning Strategies for Imaging in Southampton
✨Tip Number 1
Network like a pro! Reach out to current PhD students or faculty members in Quantum Technology Engineering. They can provide insider info and might even put in a good word for you.
✨Tip Number 2
Show your passion! When you get the chance to chat with potential supervisors, make sure to express your enthusiasm for quantum machine learning and how it relates to imaging. Let them see your excitement!
✨Tip Number 3
Prepare for interviews by brushing up on relevant topics. Dive into quantum computing strategies and classical dimensionality reduction methods. Being well-versed will help you stand out during discussions.
✨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 the opportunity.
We think you need these skills to ace PhD Studentship: Efficient End-to-End Quantum Machine Learning Strategies for Imaging in Southampton
Some tips for your application 🫡
Get Your CV Spot On:Your CV is your first impression, so make it count! Highlight your academic achievements, relevant skills, and any experience that ties into quantum technology or machine learning. We want to see what makes you stand out!
Tailor Your References:Choose references who can vouch for your skills in research or related fields. A strong reference can really boost your application, so make sure they know what you're applying for and why you're passionate about it!
Show Off Your Passion:In your application, let us know why you're excited about this PhD project. Share your thoughts on quantum machine learning and how you see yourself contributing to the field. We love to see enthusiasm and a clear vision!
Apply Early!:Don’t wait until the last minute to submit your application. The sooner you apply, the better your chances of securing funding and being considered for the position. 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 knowledge of quantum machine learning and imaging techniques. Familiarise yourself with key concepts like quantum parallelism, entanglement, and dimensionality reduction methods. Being able to discuss these topics confidently will show that you're genuinely interested in the research area.
✨Prepare Your Questions
Think about what you want to know from the supervisor, Thomas Blumensath. Prepare insightful questions about the project, potential challenges, and opportunities for personal research interests. This not only shows your enthusiasm but also helps you gauge if the PhD is the right fit for you.
✨Showcase Your Skills
Highlight any relevant experience you have in computational imaging, machine learning, or quantum computing. Be ready to discuss specific projects or coursework that demonstrate your skills and how they relate to the PhD project. This will help you stand out as a strong candidate.
✨Practice Makes Perfect
Conduct mock interviews with friends or mentors to get comfortable discussing your background and the project. Practising your responses can help reduce nerves and ensure you articulate your thoughts clearly during the actual interview.
