University of the West of Scotland Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of the West of Scotland Featured PhD Programmes

Doctor of Engineering (EngD): Evaluation of compact and low-cost sensing for rapid for biomedical and consumer healthcare using processing and machine learning techniques (STMicroelectronics (R&D) Ltd and University of Strathclyde)

   School of Engineering & Physical Sciences

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr D Li, Dr Andreas Aßmann  No more applications being accepted  Funded PhD Project (UK Students Only)

Edinburgh United Kingdom Medical Physics Optical Physics

About the Project

This work will use commercially available optical intensity and time of flight sensors to investigate a range of possibilities of rapid and real-time sensing in biomedical, consumer healthcare and wellbeing applications through the development of efficient processing algorithms and machine learning techniques.

ST has developed world-leading high-throughput ambient light and single-photon avalanche diode (SPAD) sensors suitable for biomedical applications, such as bioluminescence and fluorescence detection. ST has innovative manufacturing technologies to pack these sensors in dense 2D arrays. However, there is an urgent data challenge to be tackled to enable broader applications of ST’s new sensor technologies. Instead of using traditional off-line analysis strategies (too slow to allow real-time applications), the proposed research will focus on rapid and accurate processing of biomedical sensor data at the edge: optical intensity (light sensors) and time-resolved histogram data (SPAD sensors). What are the best algorithms and best application of machine learning for low-cost, real-time (or always on) and rapid diagnosis and detection of a range of biomedical applications.

The advent of machine learning for sensor data in a biomedical context is a rich area for research and exploration. The project will evaluate and build on state of the art bio-medical sensing techniques by adding in-situ processing and applying edge machine learning techniques to enable real-time analysis.

The student will make use of commercially available ST ambient light sensors and time of flight sensors combining them with STM32 microcontrollers (with embedded machine learning cores) or FPGA platforms. The project will have two phases. The first phase, they will investigate best processing algorithms and machine learning techniques for a range of sensing platforms: microfluidics/cell sorting, PCR, wearables (smartwatch heart rate detection, ECG), etc. Then in a second phase, take forward the most promising technique(s) and build a proof-of-concept system.

The proposed research has a cross-disciplinary nature across engineering, physics, computing and biology. Although focusing on FPGA system design throughout the project, the student will develop a theoretical framework (this is essential for repeatable experiments and broader applications) and work with colleagues with different backgrounds (optics, instrumentation, and biology) to conduct bioimaging/biosensing experiments, collect and analyse data, and disseminate the research outcomes (including IP protection and exploitation, journal and conference papers). 

Essential Criteria

A Masters level degree (MEng, MPhys, MSc) at 2.1 or equivalent. BEng 1st class considered.

Either proficiency in Verilog and VHDL with FPGA design experience or Microprocessor or Microcontroller code development (C based languages)

Desire to work collaboratively and collegiately, be involved in outreach, undertake taught, and professional skills study.

English (written and spoken) proficiency

Desirable Criteria

Good understanding of machine learning techniques.

The student will have a desk at both the University of Strathclyde (UoS) in Glasgow and in ST Edinburgh office and has the option to work at either location depending on the specific work. Working from home or on flexible hours are also options.

ST is world-leading in light and SPAD sensors; working at ST will allow the student to experience translation of ST’s state-of-the-art sensor technologies to practical life science applications. ST hosts regular research workshops and seminars where all ST-funded students can attend and report their research outcome.

Dr Li has experiences in developments of CMOS sensor systems and mixed-signal circuits. He used to work with ST on the EU MEGAFRAME project developing 160x128 and 32x32 SPAD camera systems [Veerappen et al. ISSCC 2011; Li et al., Sensors 12, 2012; Tyndall et al., IEEE Trans. Biomed. Circuits Syst., 2012; Li et al., J. Biomed. Opt. 2011; Li et al., Opt. Express 2010; Tyndall et al., ISSCC 2012]. Dr Li’s research group is specialised in FPGA embedded systems, electronic circuits, optics, instrumentation, and biosensing. It is embedded with the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS) where biosensing or bioimaging experiments can be conducted.


The CDT in Applied Photonics provides a supportive, collaborative environment which values inclusivity and is committed to creating and sustaining a positive and supportive environment for all our applicants, students, and staff. For further information, please see our ED&I statement Forming a supportive cohort is an important part of the programme and our students take part in various professional skills workshops, including Responsible Research and Innovation workshops and attend Outreach Training.

Funding Notes

Funding for this position may vary depending on the nationality/status of the applicant.