At a Glance
- Tasks: Explore machine learning to fuse 2D and 3D X-ray images for clinical diagnostics.
- Company: University of Southampton, a leader in engineering and physical sciences.
- Benefits: Competitive funding for tuition and living expenses, plus a supportive research environment.
- Other info: Join a diverse team committed to equality and sustainability.
- Why this job: Make a real impact in healthcare by advancing diagnostic imaging technology.
- Qualifications: Strong undergraduate degree in a relevant field required.
The predicted salary is between 18000 - 25000 £ per year.
Three dimensional images of the foot taken under loading conditions can provide a valuable clinical tool for the assessment of bone alignment related complaints. However, as these images have to be taken whilst a person is standing, specialised scanners are required to collect the image data. With limited availability of the required specialised equipment, most diagnostic decisions still have to be made based on traditional images, such as weightbearing two dimensional projective radiographic images or non-weightbearing three-dimensional X-ray computed tomography (CT) images, which can be generated with equipment readily available in most clinical settings.
This PhD project will explore the feasibility of combining information from several weightbearing two-dimensional projective X-ray images with non-weightbearing three-dimensional tomographic data to extract the clinically salient diagnostic information. Working closely with orthopaedic surgeons, the project is anticipated to use both simulated as well as real X-ray image data in order to develop advanced image processing and computer vision algorithms to combine information from the two modalities. Utilising the latest advances in machine learning, the project aims to overcome two fundamental challenges:
- The identification of the unknown alignment of the two-dimensional projective X-ray images relative to the X-ray imaging system.
- The identification of key anatomical landmarks in each of the images that will allow for the precise alignment of the different anatomical structures in each of the imaging conditions.
Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme, which are awarded on a competitive basis.
If you wish to discuss any details of the project informally, please contact Professor Thomas Blumensath, µ-VIS X-ray imaging centre, Email: Thomas.blumensath@soton.ac.uk, Tel: +44 (0) 2380 59 3224.
Entry Requirements
A 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: Funding for tuition fees and a living stipend are available on a competitive basis. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
How To Apply
Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Thomas Blumensath.
Applications should include:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
For further information please contact: feps-pgr-apply@soton.ac.uk
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
PhD Studentship: Machine Learning Approaches to Cross-modal Information Fusion in Podiatric X-r[...] in Southampton employer: University of Southampton
The University of Southampton is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration in the field of engineering and physical sciences. With access to cutting-edge resources and a commitment to employee growth through competitive funding opportunities, staff can thrive in a supportive environment that values diversity and inclusivity. Located in a city renowned for its academic excellence, employees benefit from a generous maternity policy, onsite childcare, and a strong focus on sustainability, making it an ideal place for meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Machine Learning Approaches to Cross-modal Information Fusion in Podiatric X-r[...] in Southampton
✨Tip Number 1
Get to know the project and the people involved! Reach out to Professor Thomas Blumensath for an informal chat about the PhD studentship. This shows your genuine interest and helps you stand out from the crowd.
✨Tip Number 2
Make sure your CV is tailored to highlight your relevant skills in machine learning and image processing. We want to see how your background aligns with the project, so don’t hold back on showcasing your achievements!
✨Tip Number 3
Don’t forget to gather strong reference letters! Choose referees who can vouch for your research capabilities and technical skills. A solid recommendation can really boost your application.
✨Tip Number 4
Apply early through our website! Since applications are considered on a rolling basis, getting your application in sooner rather than later gives you a better shot at securing that funding.
We think you need these skills to ace PhD Studentship: Machine Learning Approaches to Cross-modal Information Fusion in Podiatric X-r[...] 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 machine learning and image processing, so don’t hold back on showcasing your strengths!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about this project and how your research interests align with the work of Professor Blumensath. We love seeing enthusiasm and a clear connection to the role.
Gather Strong References:Choose referees who can speak to your academic abilities and research potential. A solid reference can make all the difference, so ensure they know what the project entails and can highlight your relevant skills.
Apply Early!:Since applications are considered on a rolling basis, we recommend getting your application in as soon as possible. The sooner you apply, the better your chances of securing funding and being considered for the position!
How to prepare for a job interview at University of Southampton
✨Know Your Stuff
Make sure you brush up on the latest machine learning techniques and image processing algorithms relevant to the project. Familiarise yourself with both two-dimensional and three-dimensional imaging concepts, as well as how they apply to podiatric assessments.
✨Show Your Passion
Express your enthusiasm for the project and its potential impact on clinical practices. Discuss any previous experiences or projects that relate to cross-modal information fusion or similar fields to demonstrate your genuine interest.
✨Prepare Questions
Have a list of thoughtful questions ready for Professor Blumensath. This could include inquiries about the specific challenges of the project, the team dynamics, or opportunities for collaboration with orthopaedic surgeons. It shows you're engaged and serious about the role.
✨Highlight Your Skills
Be ready to discuss your academic background and any relevant research experience. Emphasise your problem-solving skills and ability to work with complex data sets, as these will be crucial for tackling the challenges outlined in the project.
