At a Glance
- Tasks: Use machine learning to analyse atomic force microscopy data for cancer diagnosis.
- Company: University of Southampton, a leader in medical data analysis research.
- Benefits: Gain substantial training in scientific, technical, and commercial skills.
- Other info: Funding opportunities available; apply early for the best chance.
- Why this job: Make a real impact in cancer diagnostics with innovative technology.
- Qualifications: Strong Python skills and a good undergraduate degree required.
The predicted salary is between 18000 - 25000 £ per year.
The University of Southampton is expanding its PhD research in the area of medical data analysis. We aim to implement machine learning to analyse atomic force microscopy nanoindentation data towards automated diagnosis of cancer biopsies. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills.
We have developed a new method based on atomic force microscopy (AFM) named indentation-type atomic force microscopy (IT‑AFM), suitable for diagnostics of osteoarthritis, cancer, and atherosclerosis. The method represents a breakthrough in diagnostics and therapy, allowing for the diagnosis of structural and functional changes in tissue-related conditions at the nanometre scale.
We have published a paper in Bioengineering entitled “The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours” (https://doi.org/10.3390/bioengineering11030237) that describes the aim of the project. We are convinced that diagnostic errors, which are leading to the death of thousands of patients in the UK every year, can be significantly reduced by employing the IT‑AFM technology.
We started to make a new generation of desktop tissue analysers (DTA) to allow cancer surgeons to make better decisions. Towards bringing the IT‑AFM technology to clinical applications, the analysis of force-curves needs to be automated and fast, requiring the implementation of machine learning techniques. We have hundreds of force-maps that have been monitored by IT‑AFM on normal and osteoarthritic articular cartilage that are the basis for the above and other papers.
In a first step, we want to re-do the analysis of force-maps but now using machine learning to then compare the results with previous conventional data analysis. We aim to learn about the advantages and limitations of such an AI approach. The student needs to be enthusiastic about using Python for data analysis. The project will help you to develop skills and expertise in tool development, atomic force microscopy, and mechanobiology.
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
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.
Applications Should Include
- Research Proposal
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
PhD Studentship: Using Machine Learning To Evaluate Atomic Force Microscopy Nanoindentation Data in Southampton employer: University of Southampton
The University of Southampton is an exceptional employer, offering a vibrant research environment that fosters innovation and collaboration in the field of medical data analysis. With a strong commitment to employee development, you will receive extensive training in scientific, technical, and commercial skills, alongside the opportunity to contribute to groundbreaking research that has the potential to revolutionise cancer diagnostics. Located in a dynamic academic setting, the university provides a supportive work culture that encourages creativity and professional growth, making it an ideal place for aspiring researchers.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Using Machine Learning To Evaluate Atomic Force Microscopy Nanoindentation Data in Southampton
✨Tip Number 1
Network like a pro! Reach out to current PhD students or faculty 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
Show off your skills! Prepare a mini-project or presentation that showcases your Python prowess and understanding of machine learning. This will help you stand out during interviews.
✨Tip Number 3
Stay updated on the latest research! Familiarise yourself with recent publications in the field, especially those related to IT-AFM and cancer diagnostics. It’ll show your genuine interest and commitment.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate candidates who are eager to make a difference in medical data analysis.
We think you need these skills to ace PhD Studentship: Using Machine Learning To Evaluate Atomic Force Microscopy Nanoindentation Data in Southampton
Some tips for your application 🫡
Craft a Compelling Research Proposal:Your research proposal is your chance to shine! Make sure it clearly outlines your ideas and how they relate to the project. We want to see your enthusiasm for using machine learning in medical data analysis, so don’t hold back!
Tailor Your CV:When you send us your CV, make it relevant to the PhD position. Highlight any experience with Python, data analysis, or related projects. We’re looking for skills that align with our innovative work in atomic force microscopy.
Get Strong References:Choose referees who know your work well and can speak to your abilities in research and technical skills. A solid reference can really boost your application, so pick wisely and give them a heads-up about the role!
Apply Early Through Our Website:Don’t wait until the last minute! Apply through our website as soon as you’re ready. The sooner we receive your application, the better your chances are of being considered for this exciting opportunity.
How to prepare for a job interview at University of Southampton
✨Know Your Stuff
Make sure you’re well-versed in the specifics of atomic force microscopy and machine learning. Brush up on the latest research, especially the paper mentioned in the job description. Being able to discuss the implications of IT-AFM technology will show your genuine interest and understanding.
✨Show Your Enthusiasm for Python
Since the role requires using Python for data analysis, be prepared to talk about your experience with it. Bring examples of projects where you've used Python, particularly in data analysis or machine learning. This will demonstrate your technical skills and enthusiasm for the tools you'll be using.
✨Prepare Questions
Interviews are a two-way street! Prepare thoughtful questions about the project, the team, and the training opportunities. This shows that you’re not just interested in the position but also in how you can contribute and grow within the role.
✨Practice Makes Perfect
Conduct mock interviews with friends or mentors to practice articulating your thoughts clearly. Focus on explaining complex concepts simply, as you might need to do this during the interview. The more comfortable you are speaking about your experiences and ideas, the more confident you’ll feel on the day.
