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Electronics and Electrical Engineering
This PhD project delves into the dynamics of residential energy consumption, system flexibility, and employs the systems transition engineering processes (STEPs) to tackle energy poverty with novel utility network-to-end-use flexibility opportunities. The research is framed around the critical need to create resilient urban energy systems that not only adapt to fast-paced technological and environmental changes but also promote energy equity and efficiency.
In urban environments, residential areas are key consumers of energy and greatly influence the overall dynamics of urban energy flow. The primary aim of this research is to innovate, model and optimise the intake and distribution of energy in residential sectors and examine how these modifications can alleviate energy poverty, characterised by lack of access to reliable and affordable energy services. This involves understanding the specific energy needs of underserved populations and integrating solutions that ensure equitable energy distribution.
Transition engineering principles guide the project's approach, integrating systems thinking, predictive modelling, and simulation techniques to explore novel and practical engineering adjustments for improving system flexibility and reliability amid increasing green energy integration and fluctuating demand. Expertise will be gained in grid and network technology and commercial operations, and energy end uses—from heating and lighting to appliances and electronic devices. The project will assess initiatives like participatory demand-response technologies, energy-efficient retrofitting, integrated storage, and community energy systems.
Moving beyond technical analysis, the study will incorporate socioeconomic data to paint a more accurate picture of energy consumption patterns and barriers to energy access in various residential demographics. Simulation tools will evaluate how different interventions might impact energy affordability and reliability at the household level and their wider effects on the energy system's flexibility and sustainability.
Policy implications will also be a significant focus of this research. By identifying regulatory and institutional barriers to equitable energy distribution and system flexibility, the project aims to suggest robust policy measures that can support broad adoption of efficient and equitable energy solutions.
The expected contribution of this PhD project is pioneering energy transition shifts for adaptable, forward-thinking strategies that enhance energy system infrastructure in urban areas, ensuring that they are not only sustainable and flexible but also fair and responsive to the needs of all community members. The PhD candidate will have a Mechanical or Electric Power Engineering qualification, utility industry or energy systems engineering experience, aptitude for modelling, and passion for energy systems transition engineering. Candidates who are systems thinkers are preferred.
This PhD project is advertised as a part of the Edinburgh Research Partnership in Engineering, a joint partnership between the University of Edinburgh and Heriot-Watt University. The successful candidate will be supervised by a team consisting of academics from the University of Edinburgh and Heriot-Watt University.
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.
Essential background:
- 2.1 or above (or equivalent) in Engineering, Mathematics, Physics, Energy Engineering/Economics, Informatics, or similar
- Programming in Python, Julia or other high-level language
Desirable background:
- Energy system modelling and optimisation
- Experience in energy systems transition engineering
- Data analysis, optimisation and/or machine learning
- Experience in energy system modelling
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere
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The project aims to address a need for the Centre of Ecology and Hydrology who monitor Ammonia in the environment, especially in areas of concern such as pig farms where the high ammonia content can affect the environment.
Currently sensors of sufficient sensitivity for environmental monitoring of ammonia are not readily available for continuous readout, instead samples are collected monthly and analysed in a laboratory with no record of distribution timeline. This project will combine a previously investigated zinc nanowire detection mechanism with an optical ring resonator aiming to give continuous data at the required sensitivity enabling accurate chemical/environmental monitoring.
The successful applicant will initially work with Heriot Watt to model the device and produce a design before learning fabrication techniques in Edinburgh University cleanroom and fabricating devices. Working with the Centre of Ecology and Hydrology to expose the samplesand the performance would then be characterised at the Optics facilities at Heriot Watt.
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.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
Tuition fees + stipend are available for Home/EU and International students.
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Turbulent plumes, whether found in atmospheric gas dispersion or within high-energy fuel combustors, are central to applications across engineering, environmental science, and physics. These complex flows impact everything from air quality monitoring to fuel efficiency in combustion processes and are essential for fault diagnostics in aerospace propulsion. This project aims to explore whether it is feasible to image the concentration and temperature profile of gases within a turbulent plume using sparse optical measurements? Addressing this question requires modelling and analysis on how photons interact with highly dynamic media, combining principles from ill-posed inverse problems, and partial differential equations, such as radiative and heat transfer. Aside the harsh environment constraint that limits the amount of data that are feasible to collect, this endeavour is further complicated by the turbulent nature of the media itself. The challenge lies not only in reconstructing the complex features of these images from limited noisy data but also in establishing a rational way of parameterising and characterising the turbulence to suppress the uncertainty in the reconstructed images.
The research will delve into the study of random media and Bayesian inference frameworks as turbulent flows exhibit chaotic behaviour, and capturing data-consistent estimates of uncertainty will ensure the credibility of the reconstructed images. Moreover, the project offers an exciting opportunity to explore contemporary approaches in data-driven modelling of turbulence. Neural networks, including neural operators and diffusion models, show promise for efficiently representing the complex swirling and chaotic structures of turbulent flows, potentially offering an alternative or complement to traditional models.
This project provides a stimulating environment for mathematically inclined graduates, offering direct applicability to high-impact fields and the chance to contribute to pioneering research that bridges theory, computation, and practical application. Opportunities for direct collaboration with experts in gas metrology and aerospace to refine methods and validate results.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity
A first-class honors degree (or International equivalent) in Mathematics, Statistics or Computer Science, and preferably an MSc degree on a relevant topic, e.g., signal processing, data science.
An understanding of statistical inference is necessary. Experience with computational fluid dynamics/mechanics would be advantageous but not essential.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or Home students who are applying for scholarships from the University of Edinburgh or elsewhere.
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The Centre for Electronics Frontiers (CEF) led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified.
The project aims at exploring the potential benefits of employing software optimization techniques for modelling and training Large Language Models (LLMs), with a specific focus on Transformers, targeting various unconventional hardware architectures and computing domains (Binary, Analog, Bitstream). The project targets developing Python-based libraries for training and inference that are friendly to unconventional computing domains. These libraries should be eventually integrated with PyTorch and/or TensorFlow to facilitate modelling and quantization-aware training for different AI hardware architectures.
The required skills are as follows:
- Excellent programming skills using Python (mandatory).
- ML/AI modelling using PyTorch or TensorFlow (mandatory).
- DNN quantization and pruning techniques.
- TCL and Makefile scripting.
- Basics of computer architecture.
Group website: https://cef.eng.ed.ac.uk/
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity
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 applicants who qualify as Home applicants.
To qualify as a Home student, you must fulfil one of the following criteria:
• You are a UK student
• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme. (International students not eligible).
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The Institute of Data, Imaging and Communications is seeking a PhD candidate to explore new approaches for numerical simulation using the probabilistic framework of Monte Carlo geometry processing. This research will focus on parabolic models, particularly the advection-diffusion equation that governs gas plume dispersion in congested urban settings.
Monte Carlo geometry processing methods offer a highly flexible, parallelisable, and mesh-free approach, making them appropriate for simulating PDEs in complex or dynamically changing geometries. Unlike traditional methods such as Finite Element or Finite Difference schemes that require domain discretisation, Monte Carlo approaches allow for modelling on the edge, bypassing the need to invert large and potentially ill-conditioned matrices. Originally developed for efficient rendering in computer vision, Monte Carlo methods are now being adapted to solve PDEs across a variety of fields. Leveraging recent advancements like the walk-on-spheres and walk-on-stars algorithms, designed for elliptic models, this project aims to deliver fast-converging variants of such methods for time-dependent parabolic problems.
A key challenge in this is to establish an analogue to the mean-value property in space-time domains. Moreover, whilst Monte Carlo methods offer significant advantages in accommodating dynamic boundary conditions, they also present unique challenges. Achieving fast convergence and managing statistical noise are ongoing areas of research, as these factors are crucial for applications requiring precision and computational efficiency.
This probabilistic framework also supports inverse problem-solving, such as detecting plume sources, which involves inferring release characteristics in geometrically complex environments. Synergistically, Monte Carlo methods allow for inherent uncertainty quantification, and this is particularly useful in situations with sparse measurements or stochastic model behaviours. The topic is also amenable for cross-fertilisation of ideas from randomised numerical linear algebra, exploiting the low-dimensional structure in the kernel of the advection-diffusion model.
Potential applications for this research include environmental monitoring and national security in urban settings. This project is well-suited for candidates with a strong foundation in mathematics, stochastic processes, and Monte Carlo methods.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
A first-class honors degree in Mathematics, Statistics or Computer Science, and preferably an MSc in a closely related topic, e.g. data science or computational and applied mathematics.
A background in stochastic differential equations is necessary.
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.
Applications are welcomed from self-funded students, or Home students who are applying for scholarships from the University of Edinburgh or elsewhere.
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The Centre for Electronics Frontiers (CEF) led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified.
The project aims at exploring the potential promise of the In-Memory Computing (IMC) concept to target the bottlenecks of AI hardware. Digital IMC is proposed to bridge the Von-Neumann performance gap for AI applications where massive data workloads are consumed. However, the conventional binary computing domain degrades the benefits of IMC due to its computational complexity. The project targets exploring different unconventional computing domains (like Stochastic and Quasi-Stochastic) for IMC. Emerging technologies, with a specific focus on RRAMs, are proposed to increase the on-chip computing memory capacity.
The required skills are as follows:
- Mixed-Signal IC design using Cadence Tools (mandatory).
- Memory/SRAM design (mandatory).
- Previous experience in Tape-outs and Chip Testing.
- RRAMs and/or other Emerging Devices.
- TCL and Makefile scripting.
Centre for Electronic Frontiers (CEF) website: https://cef.eng.ed.ac.uk.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity.
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 applicants who qualify as Home applicants.
To qualify as a Home student, you must fulfil one of the following criteria:
• You are a UK student
• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme. (International students not eligible)
On
The Centre for Electronics Frontiers (CEF) led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified.
The adoption of sensory networks has been steadily increasing across various technology domains including healthcare, environmental monitoring, industrial automation, smart homes, agriculture, transportation, security, and defence. The number of these sensory nodes is projected to grow exponentially, reaching 75 billion by 2025 and escalating to 125 billion by 2030. This substantial increase will result in a vast amount of raw data that needs to be processed. This Von Neumann-like bottleneck adds more power and performance penalties to the already struggling conventional technologies in the era of AI. To mitigate this, it is crucial to adopt different unconventional technologies that span emerging electronic/photonic technologies and in-memory computing to push computational capabilities closer to the edge. Ongoing research at CEF focuses at defining a novel approach to embed intelligence locally enabling training at the edge by developing novel in-sensing processing elements (enabling electronic and photonic control). We are developing an in-sensor processing architecture using emerging devices (RRAMs) for image classification; however, it can be used in various domains such as light, RF, IR, and gas.
This PhD will be supervised by Prof Themis Prodromakis and Dr Andreas Tsiamis and aims to explore architecture routes and novel thin film materials for realising and characterising micro and nanoscale memristive devices that can be controlled electrically and optically. Device architectures include metal-insulator-metal vertically stacked structures, planar nanodevices, or hybrid architectures that extend from traditional designs. Material investigation may focus on transparent or semitransparent conducting electrodes and active single or bilayer dielectric configurations such as metal-oxides, 2D materials, organic materials etc. The PhD candidate will be trained in and consequently further develop fabrication techniques, including thin film deposition, device patterning and etching. The research will also contribute towards the development of the test apparatus and experimental procedures to allow device characterisation with optoelectronic control. Ultimately the devices may be integrated with CMOS electronics. The research is affiliated with the EPSRC programme “Pro-Sensing” that is developing next-generation semiconductor technologies for smart-imaging applications.
The successful candidate will join our team which includes researchers at the Centre for Electronics Frontiers, the Institute for Integrated Micro and Nano Systems and the wider College of Science and Engineering. They will also have the opportunity to work with our collaborators at the Institute of Photonics, University of Strathclyde. They will be based within the Institute for Integrated Micro and Nano Systems and will be trained to access our class 10 Micro and Nanofabrication cleanrooms at the Scottish Microelectronics Centre, complemented by our state-of-the-art semiconductor characterisation facilities.
Group website: https://cef.eng.ed.ac.uk/
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity
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 applicants who qualify as Home applicants.
To qualify as a Home student, you must fulfil one of the following criteria:
• You are a UK student
• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme. (International students not eligible).
On
The Centre for Electronics Frontiers (CEF) led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified.
The adoption of sensory networks has been steadily increasing across various technology domains including healthcare, environmental monitoring, industrial automation, smart homes, agriculture, transportation, security, and defence. The number of these sensory nodes is projected to grow exponentially, reaching 75 billion by 2025 and escalating to 125 billion by 2030. This substantial increase will result in a vast amount of raw data that needs to be processed. This Von Neumann-like bottleneck adds more power and performance penalties to the already struggling conventional technologies in the era of AI. To mitigate this, it is crucial to adopt different unconventional technologies that span emerging electronic/photonic technologies and in-memory computing to push computational capabilities closer to the edge. Ongoing research at CEF focuses at defining a novel approach to embed intelligence locally enabling training at the edge by developing novel in-sensing processing elements (enabling electronic and photonic control). We are developing an in-sensor processing architecture using emerging devices (RRAMs) for image classification; however, it can be used in various domains such as light, RF, IR, and gas.
This PhD will be supervised by Prof Themis Prodromakis and Dr Spyros Stathopoulos and aims to develop 3D multi-level RRAM structures for electronic and optical applications. This idea builds and expands upon our metal-oxide RRAM platform by vertically stacking functional oxide layers with varying functionalities in a Metal-Insulator-Metal-Insulator-Metal (MIMIM) fashion. Given the versatility of metal-oxides as functional materials different behaviours can be imprinted into the different active layers. These could comprise a selection layer, a memory layer and a sensory layer all independently controlled. The PhD student will develop, fabricate and characterise such structures targeting different applications for electronic and sensory elements. The research is affiliated with the EPSRC programme “Pro-Sensing” that is developing next-generation semiconductor technologies for smart-imaging applications.
The successful candidate will join our team which includes researchers at the Centre for Electronics Frontiers, the Institute of Micro and Nano Systems and the wider College of Science and Engineering. They will also have the opportunity to with our collaborators at the Institute of Photonics, University of Strathclyde. They will be based within the Institute for Integrated Micro and Nano Systems and will be trained to access our class 10 Micro and Nanofabrication cleanrooms at the Scottish Microelectronics Centre, complemented by our state-of-the-art semiconductor characterisation facilities.
Group website: https://cef.eng.ed.ac.uk/
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
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 applicants who qualify as Home applicants
To qualify as a Home student, you must fulfil one of the following criteria:
• You are a UK student
• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme. (International students not eligible).
On
The Centre for Electronics Frontiers (CEF) led by Regius Chair of Engineering Prof Prodromakis, brings together diverse and interdisciplinary expertise for transforming modern society through technology. Our ambition is to push the frontiers of electronics through emerging technologies, disrupting current ways of thinking by innovating advanced nano/biosensors, safe and efficient energy storage solutions and novel hardware for AI. We are offering prospective PhD students the opportunity to join our team, interested in devoting their passion for addressing some of the challenges we have identified.
The project aims at building large-scale AI accelerators for Large Language Models (LLMs), with a specific focus on Transformers. Diverse hardware optimization techniques can be used, targeting a scalable tile-based Tensor Processing Units (TPUs) engine with massive on-chip global buffers for data-stationary. Systolic Array (SA) architectures with novel spatial dataflows will be utilized, at a large scale, for energy-efficient LLM training and inference. The project targets building full system prototypes in advanced CMOS/FinFET technology nodes (28nm, 16nm). These digital exact computing systems are planned to host/collaborate with unconventional AI architectures with emerging technologies later.
The required skills are as follows:
- Verilog RTL Coding & Testing (mandatory).
- Digital ASIC Cell-based Flow using Cadence or Synopsys Tools (mandatory).
- Previous experience in Tape-outs and Chip Testing.
- Systolic Arrays and AI Architectures.
- SRAM design & Memory Compilers.
Group website: https://cef.eng.ed.ac.uk/
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
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 applicants who qualify as Home applicants.
Home Students:
To qualify as a Home student, you must fulfil one of the following criteria:
• You are a UK student
• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your Programme. (International students not eligible).
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