Sparse Signal Processing for Chemometrics and Hyperspectral Data Processing

This project investigates sparsity techniques for acquiring and analysing chemometric data. Chemometric data can have different modalities, including vibrational, magnetic resonance (MR) and mass- spectroscopic formats. However, the mathematical tools for data analysis  normally have a unified underlying structure. Modern chemistry needs accurate, fast and reliable techniques to process such data, which are normally subject to imperfect instrumental and experimental measurements.

Signal processing techniques extract necessary information for the high-level inference. Recent theoretical development in signal processing, computational capabilities and advances in manufacturing technologies have opened up new horizons for applications of chemometrics. This project investigates sparse signal processing methods for chemometrics, and explores low-dimensional structures for the efficient sampling, deconvolution and quantification of the data. It potentially enables us to reduce the data acquisition time, improve the accuracy and robustness of the process. As the processing time of spectral data scales up with the size of problem, this project also investigates computationally efficient methods for handling a big data setting and the computation on a portable device.

The student throughout this project will learn necessary skills to continue the research and developments in the academia or industry. Particularly, he/she learns a scientific approach to the chemometric data processing, which can be extended to other signal processing application areas, including remote sensing, oil and gas, defence and security, agriculture and food processing.

The open position is for the start in October 2017, or earlier, and it will be closed, as soon as a suitable candidate is found. The studentship fully covers the tuition fee (UK/EU level) and provides an EPSRC level stipend for 42 months, with the UK/EU candidates in priority.

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Engineering School Logo

Principal Supervisor: 

Dr. Mehrdad Yaghoobi

Assistant Supervisor: 

Prof. Mike E. Davies


The candidate is expected to have a Master’s level education (or exceptionally be an upper second-class or higher Bachelor’s degree) in electrical/electronic engineering, computer science, mathematics or closely related subjects and to fulfill the University of Edinburgh language entry requirements. The ideal candidate should also have a good mathematical background with the practical hands-on programming skills with Matlab, C/C++ or Python. A prior knowledge about sparse signal processing, chemometrics and spectroscopic methods is desirable but not necessary.

Further information on English language requirements for EU/Overseas applicants.


Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).

Further information and other funding options.

Informal Enquiries: 

Closing Date: 

Saturday, September 30, 2017