Nearly 20 years of clinical research has shown that near-infrared optical imaging based upon frequency domain diffuse optical spectroscopy (FD-DOS) can be a powerful tool for the study, diagnosis, and personalized treatment of human disease. When used for neuroimaging (i.e. functional near-infrared spectroscopy, fNIRS), FD-DOS can provide a greater imaging depth compared to standard continuous-wave (CW) fNIRS thus reaching deeper cortical layers and providing better distinction from superficial layers. In breast cancer, FD-DOS is effective for predicting individual response to neoadjuvant chemotherapy and similar strong evidence supporting FD-DOS has been collected in critical care, exercise physiology, breast cancer diagnosis, and other applications.
This project is part of a NIH funded work which involves the development and evaluation of massively-scalable multi-frequency FD hardware and software that samples tissue at ultrahigh spatial densities with high precision. It will introduces innovations in multi-wavelength optical sources and sensitive detectors, multi- frequency FD modulation and demodulation, high spatial density tissue sampling methods, FD phased-array structured interrogation of tissue, and 2D/3D image reconstruction.
This PhD is centred around
· The development of computationally intensive algorithms and methodologies to further enhance our open-source software of NIRFAST (www.nirfast.org) to allow utilization of multi-frequency modelling and data analysis.
· Development of generalized image reconstruction algorithms to utilize multi-frequency FD data and evaluate the benefits and limitation of the proposed methodologies.
· Investigation, implementation and utilisation of data reduction schemes to allow incorporation of high density, multi-frequency data for parameter recovery.
· Algorithm testing and validation using a FD-DOS system.
Key Skills
Applicants should have a very good BSc (Honours) (First or Upper Second class) degree or a Master degree (with Distinction or Merit) in either Computing Science, Physics or related discipline.
Essential Knowledge and Experience:
· Programming experience (preferably some in Matlab and Python and some basic image processing techniques)
· Strong communication skills (including written/spoken English)
· Some experience in numerical modeling, particularly Finite Element models
· Solid skills in maths/statistics
· An ability to think independently and critically analyse different sources
Desirable requirements
· Knowledge of deep learning techniques/packages (e.g. Keras, TensorFlow,)
· Experience in the development, application and deployment of signal processing techniques
Applicants should have good personal and communication skills, strong professionalism and integrity, and be capable of working on their own initiative.
Enquiries can be emailed to using the link below from findaphd.com.
Applications
The applications should consist of a covering letter or personal statement of interest, a CV and an outline of research proposal, which you can submit online via
https://www.birmingham.ac.uk/schools/computer-science/postgraduate-research/index.aspx
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