PhD Studentship: Machine learning algorithms for autonomous multiparametric imaging and analysis of DNA molecules and interactions
Marie Skłodowska-Curie Actions doctoral network - spm 4.0
Deadline: February 2025. Fully funded position with stipend, open to international students. This position comes with a generous salary for a PhD candidate of £41,311, funded by the European commission. You can find more information on the applications here.
We are seeking applications for a Research Fellow, in a Horizon Europe MSCA-Doctoral Network (DN) program (Funded by EU, GA: 101168976) entitled “Autonomous Scanning Probe Microscopy for Life Sciences and Medicine powered by Artificial Intelligence (SPM4.0)”.
The objective of the SPM4.0 Doctoral Network is to train a new generation of researchers in the science and technology of autonomous Scanning Probe Microscopes powered by Artificial Intelligence for applications in the Life Science and Medical fields. The researchers of the network will acquire state-of-the-art multidisciplinary scientific training in advanced scanning probe microscopy and machine learning and in their biological and medical applications. In addition, they will receive training on complementary and transferable skills to increase their employability perspectives and to qualify them to access leading job positions in the private and public sectors. The final aim is to promote the wide adoption of SPM4.0 technologies in public and private research centres and in industrial and metrology institutions and to explore new horizons in the Life Sciences and Medical sectors spanning label-free nanoscopic cell imaging, illness diagnosis, or drug nanocarrier development, consolidating Europe as a world leader.Over the past 60 years, we have discovered many of the rules which determine how our genetic makeup affects our health, from Rosalind Franklin’s pioneering discovery of the helical structure of DNA, through to the human genome project. However, we still do not have the tools to measure DNA’s secondary, mechanical code, which affects nearly all interactions and therefore our wellbeing at a cellular level. This is due in part to the complexity of cellular DNA, caused by its innate flexibility, compaction in the nucleus, and manipulation by DNA-processing enzymes. These processes cause DNA to adopt a vast range of intricate structures, conformations and topologies which are hard to quantify as they occur at the nanometre length scale.
This project will develop new deep-learning image analysis methods to identify, segment and trace individual DNA molecules from topographic images.
Objectives:
• To train and benchmark machine learning models for AFM imaging of DNA molecules interacting with a range of proteins (collaborating with Gwyddion).
• To develop a convolutional neural network to characterise DNA and protein structures in topographic AFM images and define scanning areas around them (collaborating with IBEC)
• To contribute to integrating the neural networks developed into the post-processing software (collaborating with DATRIX)
Planned secondments
• Sorbonne University, M18 (1M) building indexed big databases.
• The Czech Metrology Institute, M24 (1M) Gwyddion programming.
• Bruker, T. Mueller, M36 (2M) multiparametric measurements.
Experimental Approach:
The project will use and develop our Python pipeline TopoStats, integrating machine learning approaches to quantitatively determine the mechanical state of individual DNA molecules.
You will be supervised by Alice. All supervisors are committed to embedding positive and inclusive research cultures in their groups. The supervisors will work together to ensure expectations on students and of supervisors are clearly defined and communicated. We welcome applicants from a diverse range of backgrounds across the physical and biological sciences and engineering with a background in programming.
We welcome applicants from a diverse range of backgrounds across the physical and biological sciences and engineering. Interested candidates are strongly encouraged to contact Alice to discuss your interest in and suitability for the project prior to submitting your application. Please refer to the SPM4.0 webpage for detailed information about the network and you can apply here.
Funding Notes
The award will fund a 3-year full-time employment contract with a gross salary of €XXX approx, depending also on the family status at the moment of recruitment, according to the MSCA-DN regulations, enrolment in a PhD programme at the University of Sheffield as well as a research grant to support costs associated with the project.