PhD Studentship: Using Machine Learning To Evaluate Atomic Force Microscopy Nanoindentation Data

Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data  

Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi

Project description:

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, and allows 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, 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 AI-approach. The student needs to be enthusiastic using Python for data analysis.

The project will help you to develop skills and expertise in tool development, atomic force microscopy, 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.

How To Apply

Apply online:  HERE 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 Martin Stolz.

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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