Integrated Micro and Nano Systems
Research Themes
Aim
Our aim is to study trade-offs in image compression algorithms between robustness and several task-specific metrics, like rate, classification error, perception quality, and reconstruction performance, in order to gain insights to develop robust algorithms, with application in extreme compression scenarios, like underwater communication.
Objectives
- Develop a theoretical characterisation of the tradeoffs in image compression between robustness, compression rate, classification error, perception quality, and reconstruction performance.
- Design new image compression algorithms that select features according to the task at end and that are robust to adversarial attacks.
- Validate the algorithms in realistic scenarios, where images undergo extreme compressed due to limitations in bandwidth, as in underwater communication.
Description
Deep neural networks (DNNs) have become essential tools in automated decision-making, powering applications from image classification and segmentation to anomaly detection and portfolio allocation. However, DNNs are notoriously vulnerable to adversarial attacks, prompting extensive research over the past decade to develop new attacks and defense strategies [1-4]. Whilst implementing these defenses can reduce performance on non-perturbed samples [5-7], their impact on multi-task performance remains unclear.
In this project, we explore image compression and decompression algorithms where the target image serves various tasks, such as classification, segmentation, or multimedia. By leveraging knowledge of these downstream tasks, the encoder can select better features and achieve improved compression rates [8]. However, there are inherent trade-offs between compression rates and task performance [9]. Our goal is to study how adversarial attacks influence these tradeoffs, design robust mechanisms for the entire pipeline, for example, leveraging semantically-meaningful representations, and demonstrate the application of these algorithms in scenarios requiring extreme compression ratios, such as underwater communication.
[1] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, "Intriguing Properties Of Neural Networks," arXiv:1312.6199v4, 2014.
[2] N. Akhtar, A. Mian, N. Kardan, and M. Shah, ‘Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey’, IEEE Access, vol. 9, pp. 155161–155196, 2021, doi: 10.1109/ACCESS.2021.3127960.
[3] A. Modas, R. Sanchez-Matilla, P. Frossard, and A. Cavallaro, ‘Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective’, IEEE Signal Process. Mag., vol. 37, no. 4, pp. 14-23, Jul. 2020, doi: 10.1109/MSP.2020.2985363.
[4] D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, et al., "The Many Faces Of Robustness: A Critical Analysis Of Out-of-Distribution Generalization," ICCV, pp. 8340-8349, 2021.
[5] D. Tsipras, S. Santurkar, L Engstrom, A. Turner, A. Madry, "Robustness May Be at Odds with Accuracy," ICLR, 2019.
[6] H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, M. Jordan, "Theoretically Principled Tradeoff Between Robustness and Accuracy," ICML, PMLR 97:7472-7482, 2019.
[7] M. Mehrabi, A. Javanmard, R. A. Rossi, A. Rao, T. Mai, "Fundamental Tradeoffs in Distributionally Adversarial Training," ICML, 2021.
[8] Z. Lei, P. Duan, X. Hong, J. F. C. Mota, J. Shi, and C.-X. Wang, "Progressive Deep Image Compression for Hybrid Contexts of Image Classification and Reconstruction", IEEE J. Select. Areas Commun., vol. 41, no. 1, pp. 72–89, Jan. 2023, doi: 10.1109/JSAC.2022.3221998.
[9] J. Fang, J. F. C. Mota, B. Lu, W. Zhang, and X. Hong, "The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs", IEEE Trans. Signal Process., vol. 72, pp. 3076-3090, 2024, doi: 10.1109/TSP.2024.3411692.
[10] A. Wheeldon and A. Serb, "A study on the clusterability of latent representations in image pipelines," Front. Neuroinform, vol. 17, no. 1074653, pp. 1-11, 2023. doi: 10.3389/fninf.2023.1074653
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Home rate fees and stipend are available for this position.
Research Theme
Aim
To develop a low-power, organic photovoltaic (OPV)-integrated, disposable sensor platform that detects trace vapours associated with explosives and common environmental gases (NOx, NH₃, CO and VOCs) for resilient, distributed monitoring.
Objectives
- Design and print a multi-modal sensing array (chemiresistive) with selective coatings for nitroaromatics, taggants and key environmental gases.
- Develop an OPV power-management system and low-power electronics to enable duty-cycled, ≥24-hour autonomous operation under realistic illumination.
- Develop machine learning and pattern-recognition methods to classify sensor responses, enhance selectivity, and minimise false alarms under variable environmental conditions.
- Validate performance with safe simulants in the lab.
Description
This project will develop a compact, low-power sensor node that integrates printed organic photovoltaics (OPV) with a multi-modal array of disposable chemiresistive and electrochemical sensing elements to detect trace vapours from nitroaromatics, explosive taggants, and key environmental gases including NOx, ammonia, carbon monoxide and common VOCs. Functionalized carbon-nanomaterial and molecularly imprinted polymer coatings will provide selective uptake, while machine learning and pattern-recognition algorithms will fuse multi-sensor responses to maximise sensitivity, discriminate analytes, and reduce false alarms. To ensure sustainability, devices will be designed to safely disintegrate into soil after use, building on Dr Panidi’s expertise in organic electronics. A key challenge for low-cost sensors is accuracy in complex, dynamic environments; to address this, deep learning approaches such as convolutional neural networks (CNNs) will enhance feature extraction and enable robust, real-time pollutant classification, drawing on Dr Yang’s expertise. This project couples sustainable sensor design with AI-driven analytics, directly supporting DSTL’s priorities in scalable, secure environmental sensing for public health and national resilience.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position.
Aim
This project will establish a new paradigm of magneto-photonic neuromorphic devices, directly linking advances in fundamental spin dynamics, reservoir computing (RC) approaches and advanced materials to pressing technological challenges, such as the unsustainable rise in energy consumption from information and communication technologies (ICTs).
Objectives
- Demonstrate heterostructures formed by quantum magnets + plasmonic layer + substrates as nonlinear, memory-rich dynamical elements suitable for RC.
- Develop and compare device architectures: (i) time-multiplexed single-node reservoirs, and (ii) spatially coupled magnetic arrays.
- Integrate photonic and magnonic coupling for scalable, multimodal reservoirs.
- Quantify computational performance and efficiency on benchmark tasks and against CMOS and photonic RC standards.
- Establish design rules for future hybrid magneto-photonic neuromorphic processors.
Description
The exponential growth of ICTs is driving energy consumption towards unsustainable levels, motivating radical innovations in low-power information processing. Neuromorphic and RC offer promising routes to energy-efficient computation by exploiting the intrinsic dynamics of physical systems, rather than relying on conventional von Neumann architectures.
Novel device concepts that unite non-rare element magnets, photonic layers and CMOS-friendly substrates presents an untapped opportunity in this context. Its defining attributes—ultrafast optical control of spin states, strong nonlinearity from magnetization precession and spin-phonon interactions, and multi-modal (spin, phonon, photon) coupling—map directly onto the requirements of RC. In this context, RC leverages a high-dimensional, nonlinear dynamical system as a “reservoir” to transform inputs into separable states, requiring only a linear readout for training. Leveraging these properties with novel quantum materials enable the creation of magnetic reservoir processors that combine low-energy operation with ultrafast bandwidth and dense integration, addressing urgent demands for sustainable AI and edge computing.
This project will establish a new paradigm of magneto-photonic neuromorphic devices, directly linking advances in fundamental spin dynamics to critical technological challenges. The proposed research will:
- Advance fundamental knowledge of nonlinear spin dynamics under ultrafast optical control, with cross-disciplinary implications for condensed matter physics, spintronics, and photonics.
- Enable transformative technologies for neuromorphic and edge computing by combining ultralow energy operation with ultrafast temporal response.
- Contribute to sustainability by addressing the rapidly growing energy footprint of ICTs, aligning with global efforts towards net-zero digital infrastructure.
- Train a new generation of researchers at the intersection of magnetism, photonics, and neuromorphic computing.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position
Developing renewable energy solutions that can be rapidly implemented in the market using eco-friendly materials and manufacturing methods is crucial. Among various renewable technologies, photovoltaics have significant potential to support climate change mitigation. Organic photovoltaics (OPVs) have recently attracted considerable attention due to a new family of semiconductors that enable highly efficient light harvesting in both indoor and outdoor environments. Additionally, OPVs offer a low carbon footprint and high recyclability potential.
However, a current limitation is the use of toxic solvents and materials in manufacturing. Most organic electronic devices require halogenated and non-halogenated aromatic solvents, which are often carcinogenic or toxic to human reproductive systems and harm the environment. For large-scale production and commercialization, this is a critical issue.
This project aims to enhance the performance of OPVs through engineering strategies, eliminate the use of toxic materials and implement methods to enhance their stability. In addition, thin films and OPVs will be evaluated with a series of optoelectronic and morphological characterisation tools.
The PhD candidate will be supervised by Dr Julianna Panidi (School of Engineering) and Dr Yue Hu (School of Chemistry).
The successful candidates will join our team, which includes researchers from the Centre for Electronics Frontiers, the Institute for Integrated Micro and Nano Systems, the School of Chemistry, and the wider College of Science and Engineering.
Before you apply: We strongly recommend that you contact the supervisor for this project before you apply.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU and International students