PhD Scholarship in Application of Novel Artificial Intelligence and Machine Learning Methods for Innovative and Safe Microwave-based Imaging Technology

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PhD Scholarship in Application of Novel Artificial Intelligence and Machine Learning Methods for Innovative and Safe Microwave-based Imaging Technology

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[ad_1] Description:  Since 2000, radiofrequency technology in the microwave band (1-10 GHz) has been suggested for applications in the

Senior Lecturer in Adult Nursing at Leeds Trinity University
Lecturer in Pre-Qualifying Practice at Birmingham City University
Research Associate/ Fellow (multiple posts, part time) at University of Nottingham

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Description: 

Since 2000, radiofrequency technology in the microwave band (1-10 GHz) has been suggested for applications in the medical field. When used for diagnostic imaging, microwaves allow to obtain millimetric resolution, exploiting the contrast in dielectric properties. Microwaves are non-ionising; therefore, repeated imaging applications would be safe. Microwaves can penetrate through air, skin, bones, and tissues, which are an obstacle for other technologies such as ultrasound. Microwaves technology has a low-cost, hence making realization of cheaper, smaller, and more portable devices a possibility.  

This PhD project aims to investigate (analytically and theoretically), design, and characterize (via simulation and/or experimentally) an advanced system which could be used for diagnostic imaging. Dedicated microwave image reconstruction techniques from multiple antenna beams will be studied and the mathematical formulation derived. Moreover, sophisticated novel artificial intelligence and machine learning methods for Innovative and safe microwave-based imaging technology will be proposed and implemented by the student. Verification of the developed modality will be confirmed by the available devices at LSBU and in related clinical examinations.

The outcomes of this project for the PhD candidate are listed below:

  • Gain knowledge and experience in 2D and 3D microwave image reconstruction techniques.
  • Design and characterize a microwave system which could be used for diagnostic imaging.
  • Apply novel artificial intelligence and machine learning methods for Innovative and safe microwave-based imaging technology.
  • Perform system validation via simulation/experimental measurements, assessing imaging resolution.
  • Gain multidisciplinary experience form turning research outcomes into medical devices.
  • Present the findings of the project in international conferences.
  • Perform high-quality research and publish it as journal articles.

This will be a 3-year fully funded studentship for an EU/UK and overseas applicants who are keen to conduct research in radiofrequency technology applied to the medical field. Research will be carried out at the LSBU School of Engineering in cooperation with the industrial partner UBT Srl, Italy.

Supervisory Team: The successful applicant will be working at LSBU with Prof Mohammad Ghavami (peoplefinder.lsbu.ac.uk/researcher/806yq/dr-mohammad-ghavami) and Dr Gianluigi Tiberi, who will act as the industrial supervisor.

As a PhD student, the successful applicant will join the South Bank Applied BioEngineering Research (www.lsbu.ac.uk/research/centres-groups/bioengineering) and will work alongside a range of new and experienced PhD students in a vibrant, collaborative, multidisciplinary and international environment.

Informal enquiries should be directed to Prof Mohammad Ghavami (ghavamim@lsbu.ac.uk). Please send a copy of your CV with a covering letter directly to Prof Mohammad Ghavami before applying.

Requirements: Applicants must be of outstanding academic merit and should have either a first class or an upper second-class Honours degree (or the international equivalent), or an MSc/MRes with distinction. Enthusiastic and self-motivated candidates from all countries with a background in either Electrical Engineering, Biomedical Engineering, Physics, or a related discipline are encouraged to apply. A good knowledge of Matlab and experience in artificial intelligence and machine learning for microwave imaging would be advantageous.

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