NeuMat Summer Undergraduate Studentships 2026

Project Summaries

Project: Neuromorphic Computing and Sensing with Ferroelectric MEMS

Supervisors: Mohammad Zaid, Prof. Ashwin Seshia

Host: Dept. of Engineering, University of Cambridge – Electrical Engineering Division

APPLY HERE

Project: From Neurocomputing Foundation to SNN Prototyping: An 8-Week Python-Based Project with an HDL Extension

Supervisor: Dr Somayyeh Timarchi

Designed as an 8‑week full‑stack neuromorphic computing pipeline, this project trains the student to build and validate LIF/Exp‑LIF neurons, implement SNN classifiers with STDP learning, and ultimately prototype fixed‑point HDL modules for hardware‑aware verification. The work emphasises reproducible engineering, cross‑stack reasoning, and rigorous validation workflows. The student will contribute both a functional Python codebase and a hardware‑verifiable neuromorphic building block aligned with NeuMat’s systems‑to‑materials vision.

Host: Queen Mary University of London – School of Electronic Engineering & Computer Science

APPLY HERE

Project: Spiking Neural Units for Sequence Learning

Supervisors: Emre Sahin, Edoardo Altamura

This project immerses the student in the Spiking Neural Unit (SNU) framework, reproducing canonical neuromorphic benchmarks before extending SNU‑based recurrent models to molecular property prediction tasks. With a strong emphasis on reproducibility, systematic benchmarking, and algorithm‑to‑hardware reasoning, the work positions SNUs as a bridge between deep learning and neuromorphic dynamics. Outputs include a documented codebase, comparative results, and a conference‑ready poster or short paper.

Host: The Hartree Centre, STFC

APPLY HERE

Project: Fully Memristive Spiking Neural Network with Forward‑Forward Learning

Supervisor: Dr Xinming Shi

This project develops a fully memristive SNN in which both neuron and synapse behaviours emerge from RRAM device physics. Using the Forward‑Forward (FF) algorithm as a local, hardware‑aligned learning rule, the student will build compact SNN architectures, validate device‑calibrated models, and benchmark performance on neuromorphic datasets including DVS inputs. Situated within QUB’s strong neuromorphic hardware ecosystem, the placement integrates device‑level modelling, system design, and practical experimentation.

Host: Queen’s University Belfast – School of Electronics, Electrical Engineering & Computer Science

APPLY HERE

Project: Simulation of Multimodal Thin‑Film Transistors for Analogue Memristor Networks

Supervisors: Dr Radu Sporea, Dr Eva Bestelink

Using Silvaco TCAD, the student will simulate multimodal thin‑film transistors (MMTs) as selector/current‑limiter elements for analogue memristor arrays. The project addresses parasitics, dynamic biasing, and mixed‑signal behaviour crucial for robust compute‑in‑memory architectures. With strong interdisciplinary supervision and collaboration links to UCL and Cambridge, this work lays the foundation for future EPSRC‑level proposals in analogue neuromorphic hardware.

Host: Advanced Technology Institute, University of Surrey

APPLY HERE

Project: Neuromorphic Optomemristor Chips for Cryogenic AI Systems

Supervisor: Dr Firman Simanjuntak

This experimentally focused project characterises optically addressable memristive synapses under cryogenic conditions relevant to space, quantum, and secure‑AI hardware. The student will study switching behaviour, noise resilience, and synaptic properties at deep‑cooled temperatures and use extracted device models to implement simple neuromorphic tasks. The project leverages Southampton’s strong device‑characterisation facilities and industrial ties with Von Ardenne GmbH.

Host: University of Southampton – School of Electronics & Computer Science

APPLY HERE

Project: Event‑Driven Monitoring of Plant Growth and Stress Using Brain‑Inspired Sensing

Supervisor: Dr Rachel Thorley

This interdisciplinary project applies neuromorphic principles to agricultural sensing by detecting plant growth and stress through event‑driven image analysis. Working with Arabidopsis time‑lapse data—and optionally running a validation experiment—the student will develop lightweight change‑detection algorithms and compare event‑driven vs continuous approaches. Embedded within a supportive, cross‑disciplinary ecosystem, the project connects plant science, energy‑efficient sensing, and edge neuromorphic computation.

Host: University of Cambridge – Churchill College, Department of Engineering

APPLY HERE

Project: Non‑Ideal Effects in Memristor‑Based, Training‑Free One‑Shot Learning

Supervisors: Prof. Andreas Demosthenous, Tianyang Yao

This simulation‑driven project examines how memristor non‑idealities—write errors, stochasticity, variability—can improve training‑free one‑shot learning by acting as inductive biases in feature extraction. The student will implement Python‑based reservoir‑style models, extend device equations to include imperfections, and quantify accuracy under varying non‑ideality levels. Hosted in UCL’s Bioelectronics Group, the placement integrates device physics, neuromorphic algorithms, and system‑level learning analysis.

Host: UCL Department of Electronic & Electrical Engineering

APPLY HERE

Project: Spiking Neuron Models for Optical Communication DSP on FPGA

Supervisors: Geraldo Gomes, Pedro Freire, Sergei Turitsyn

This project investigates whether LIF‑based spiking neuron models can serve as computational primitives for digital signal processing in coherent optical communication systems. The student will implement fixed‑point LIF neurons on FPGA and evaluate their stability, temporal memory, and robustness using optical‑DSP‑inspired signals. Connecting neuromorphic theory with hardware‑validated telecom applications, the project offers training across FPGA design, neuromorphic modelling, and high‑throughput signal processing.

Host: UK Multidisciplinary Centre for Neuromorphic Computing (Aston University)