PhD Studentship: Natural Computing with MEMS Neuromorphic Reservoirs for In-Sensor Intelligence

Imagine a world where sensors don’t just collect data, they think. With the exponential growth of the Internet of Things (IoT) and edge computing, the demand for ultra-low-power systems that can sense, and process information locally has never been greater. Traditional architectures that separate sensing and computation create bottlenecks in speed and energy efficiency. This PhD project aims to break that barrier by developing a groundbreaking MEMS-based neuromorphic platform that physically implements Reservoir Computing (RC), a bio-inspired approach using nonlinear dynamics for fast, efficient, real-time processing.

Your research will focus on integrating sensing and computation into a single MEMS device, designing a physical RC system that exploits the rich nonlinear dynamics of MEMS resonators, and harnessing phenomena such as modal coupling to boost computational power. The goal is to create novel MEMS neuromorphic sensor that combines sensing and computing in one device, reducing latency and energy consumption.

You will validate this breakthrough through tasks such as MNIST handwritten digit recognition and real-world gas sensing, proving simultaneous sensing and computation in action. This project will establish a new paradigm for neuromorphic sensors, paving the way for next-generation, low-power solutions for IoT and edge AI.

By completing this PhD project, you will not only push the boundaries of IoT and edge computing but also position yourself as a future leader in MEMS technology and natural computing.

Supervisors:
Primary supervisor: Amal Hajjaj

Secondary supervisor: Stephanos Theodossiades

Entry requirements:
A minimum of an upper-class honours degree (2:1) or overseas equivalent in Engineering or Physics, or equivalent relevant industrial experience.

English language requirements:
Applicants must meet the minimum English language requirements.

Funding information:
The studentship is for 3 years and provides a minimum tax-free stipend of £20,780 per annum (2025/26 rate) for the duration of the studentship plus university tuition fees.

Funding will be awarded on a competitive basis and is not guaranteed; availability will depend on the outcome of the selection process and subject to final approval by the University.

The following selection criteria will be used by academic schools to help them make a decision on your application: https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/studentship-assessment-criteria/.

How to Apply:

All applications should be made online. Under programme name, select Mechanical and Manufacturing Engineering. Please quote the advertised reference number: FP-SA26-AH in your application.

Applications must include a personal statement, up-to-date curriculum vitae (CV), details of two referees (one from your highest degree qualification), certified certificates and transcripts for all completed degree programmes, and a reference to the project FP-SA26-AH. Submission of a Research Proposal is not essential but may strengthen your application. Incomplete applications received after the closing date may not be considered for interview.

Shortlisted candidates will be contacted for an interview, which are expected in February/early March 2026.

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