About the Project
The PhD is a combined theoretical and experimental project which is aiming to develop a heuristic approach for a non-evasive in-vivo early stage cancer detection. It will be achieved by developing a “proof-of-principle” physical implementation of an Artificial Neural Network (ANN) – not as software – on a photonic chip inspired by the how brain handles and processes information.
The proposed “photonic brain” will be based on the recent innovation in ANN technology. The optical-physical implementation of ANN will allow an artificial intelligence computing directly to the optical signal in a photonic chip. The envisaged device will serve as a “plug-and-play” device, hence to enable a swift integration to a current conventional diagnostic instrument.
The project:
The candidate is expected to design a photonic artificial neural network system on a photonic integrated circuit platform. This is done by developing an analytical and/or numerical model to predict the impact of external perturbation to the system performance. The candidate will prototype and characterised the photonic ANN system. The candidate will have access to the GGIEMR Institute’s supercomputer and dedicated laboratories for the fabrication and testing of mid-IR fibre, chips, and systems, including a class 10,000 cleanroom. Facilities include: a customised Heathway fibre draw-tower, advanced glass melting, glove boxes, sputter-coating, extrusion, thermal analysis, a comprehensive laser-suite, dedicated optical bench for optical waveguide and component characterisation including mid-IR optical components, photoluminescence, Fourier-transform IR spectroscopy, refractive index measurement and near-field imaging.
The ideal candidate will have:
1. a first or upper second class honours or Masters degree in in Physics, Applied Physics, Electrical and Electronic Engineering, Mathematical Sciences, or a related subject and equivalent degree from a quality recognised institution.
2. a solid background in design and/or experimental photonics/optoelectronics. Previous experience in using photonic design software (Lumerical, CST, MEEP or HFSS) will be an advantage.
3. a solid background in electromagnetics, mathematics and excellent analytical and numerical skills, as well as problem solving skills
4. strong programming skills in Matlab, C/C++, or Python. Previous hands-on experience with deep learning platforms will be an advantage.
5. very good written and communication skills and fluency in English.
6. a driven, independent professional and self-reliant work attitude within a fast-paced & collaborative environment.
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
How to apply:
Applicants must obtain the support from Dr Sendy Phang prior to submitting their application.
Informal contact can be sent to Dr Sendy Phang ([Email Address Removed]) before submitting an online application. Please (i) insert your cover letter, CV and copies of academic transcripts into a single pdf file. (ii) Name the file with your name as ”firstName_lastName_phd”. (iii) e-mail to: [Email Address Removed], with [Neuromorphic Photonics PhD application - lastName] as the email subject.
Application instructions:
Formal applications are to be made via http://www.nottingham.ac.uk/pgstudy/apply/applyonline.aspx. Your application should include;
(1) a brief cover letter that describes your reasons for wishing to pursue a PhD, a statement of research interests, and an agreement in principle with Dr Sendy Phang that he is ready to supervise you. Please include the reference number (beginning ENG) within the cover letter;
(2) a copy of your CV, including actual or expected degree class(es), a transcript of all University results, a list of publications, and contact details for two academic referees. Applications without academic transcripts or academic referees will not be considered;
(3) copies of any publications or an example of your technical writing, such as a project report or dissertation;
Shortlisted candidates will be invited for a Skype interview.
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