"PhD (IDEAL) Studentship: On-Device Monitoring and Adaptation Design for Embedded Systems"

Background

The CO2 emissions from manufacture (the so-called embodied carbon) of end-user ICT devices makes up the majority of their carbon footprint over the typical current useful life of such devices.

To achieve sustainable ICT, in addition to reducing run time energy consumption, it is therefore essential to extend the active life, ideally to several decades.

The project

The PhD project aims to develop a novel adaptive embedded hardware system, which equipped with a low-level hardware monitoring system together with a multi-level intelligent algorithms and diagnostics capabilities for adaptation of embedded software and hardware during run-time.

The proposed solution will not only be providing the ability to reliably operate the embedded systems in complex practical environment with cost-effective manner, but also accurately assess the behaviours of any given device, enabling the repurposing of devices with reduced capabilities for new tasks, therefore, it will enable a drastic reduction in the embodied carbon of IoT devices, and the circular economy, as our technology will allow devices to be repurposed repeatedly throughout their useful life.

Funding

This project is supported by the School of Computer Science and Electronic Engineering (CSEE) in the University of Essex as part of UKRI / EPSRC funded £1.8M IDEAL project.

The studentship will provide the PhD candidate with a costs of living stipend equal to UKRI rates (£20,780 for 2025/26), plus travel and training allowances via Proficio and full UK/International tuition fees waiver.

The successful candidate will need to start the PhD at the Essex in 2026.

Person specification

• The successful candidate will meet our English Language requirements and will have a BSc or BEng honours degree (UK 1st class or equivalent) in computer science, electronic engineering or a related subject. An MSc with Merit or Distinction is desirable (but not essential for students with a first-class undergraduate degree).
• Strong analytical and mathematical skills are required, as well as good programming skills in C/C++, VHDL/Verilog, or similar hardware description language (HDL) language. Knowledge of microprocessor architecture, machine learning, field-programmable gate array (FPGA), HDL, and/or embedded systems are essential. Knowledge of Python for machine learning and AI techniques are desirable not essential.
• While this studentship is open to all applicants, we encourage applications from female candidates and/or students from less privileged backgrounds from developing countries.

To apply, please click on the ‘Apply’ button above.