Electronics and Electrical Engineering

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.

Further information and other funding options.

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)

Further information and other funding options.

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).

Further information and other funding options.

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).

Further information and other funding options.

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).

Further information and other funding options.

Soft robotics represents a transformative approach to creating robots that are inherently safer and more adaptive to real-world environments. Unlike traditional rigid robots, soft robots can deform, stretch, and interact with humans or delicate objects in a more natural and flexible manner. As these robots become increasingly vital in fields ranging from healthcare to industrial automation, one of the key challenges is developing advanced sensing technologies that seamlessly integrate with soft, flexible materials to provide real-time feedback for precise control and decision-making.

This PhD project aims to address this challenge by developing integrated fabrication techniques that embed sensing technologies directly into soft robotic structures. By combining expertise in flexible electronics, advanced materials, and 3D-printing-based fabrication methods, this project will enable the creation of multifunctional soft robotic systems with enhanced sensing capabilities. These systems will be capable of detecting touch, pressure, deformation and other physical parameters crucial for dynamic interaction with their environments.

This project will be carried out at the SMART Group within the Institute for Imaging, Data and Communications (IDCOM) and closely collaborate with the leading robotics researchers from the Soft Systems Group. This research provides an exciting opportunity to work at the intersection of materials science, robotics, electronics and machine learning. The ideal candidate will have a background in electronics/mechanical engineering, materials science, robotics, or a related field, with a strong interest in soft robotics and sensing technologies. By joining this project, you will contribute to the rapidly growing field of soft robotics and help develop systems that could reshape healthcare, manufacturing, and other industries.

Due to a multidisciplinary nature of this project, it will be supervised by an academic team that have expertise in different engineering areas. The supervision team will consist of:

Principal supervisor:

Dr Yunjie Yang (Imaging, Data and Communications), https://www.eng.ed.ac.uk/about/people/mr-yunjie-yang

Assistan supervisors:

Professor Adam Stokes (Integrated Micro and Nano Systems): https://www.eng.ed.ac.uk/about/people/professor-adam-stokes;

Dr Francesco Giorgio-Serchi (Integrated Micro and Nano Systems): https://www.eng.ed.ac.uk/about/people/dr-francesco-giorgio-serchi

Dr Michael Chen (Bioengineering) https://www.eng.ed.ac.uk/about/people/dr-michael-xianfeng-chen

Please also note that this advert will close as soon as a suitable candidate is found. Successful candidate will be expected to start in September 2025. 

Please make sure to submit a research proposal as part of your application. For advice on writing a Research Proposal please see here:

https://www.ed.ac.uk/files/imports/fileManager/HowToWriteProposal090415.pdf

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* but exceptional international students will also be considered.

Applications are also welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Home Students

To qualify as a Home student, you must fulfill 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.

Further information and other funding options.

As AI and data centers expand, their energy demands are becoming a significant sustainability issue, with AI projected to consume 85-134 TWh of electricity by 2027—comparable to a small country's annual usage. The growing adoption of AI models like OpenAI’s ChatGPT and Google’s Bard underscores the need to reduce data centers' carbon footprints. Future AI data centers will focus on low-carbon strategies to mitigate their environmental impact.

This cross-disciplinary PhD program in electrical engineering and computer science aims to develop real-time strategies for minimizing the carbon footprint of data centers. The research will optimize the jobs scheduling at large scale data center, addressing the challenge of managing high volumes of computational tasks required by large AI models. Advanced machine learning, optimization, and data analytics techniques will be explored to enable a more proactive response to the electricity supply profile, ensuring that data center operations are effectively integrated with the broader energy network.

A key component of the program is exploring how AI data centers can support energy networks, providing grid services like frequency and demand response during periods of low-carbon electricity or when the grid requires ancillary services. This research seeks to enhance the interaction between data centers and the electricity grid, promoting better coordination and contributing to grid stability and efficiency.

The solution will focus on developing practical, low-carbon data center operations for real-world deployment. By improving demand response services, data centers can operate more sustainably while helping stabilize the grid. This research is vital for addressing the energy demands of AI systems and ensuring that future AI developments align with global sustainability goals.

To apply for this position, It will be an advantage if applicants have relevant industry or research experience, or good programming skills using Python, Julia or matlab. Strong knowledge in one or more of the following areas is highly desirable:

• Power network modelling and control

• Optimization, data science, operational research and mathematical programming.

• Machine Learning and Reinforcement Learning

• Computer science with understanding of data center structure and operation

**NOTE: There is no closing date for this position, which will remain open until filled.  Early contact is highly recommended.

Reference:

   ‘Generative AI’s Energy Problem Today Is Foundational’ https://spectrum.ieee.org/ai-energy-consumption   Misaghian, M. Saeed, et al. "Assessment of carbon-aware flexibility measures from data centres using machine learning." IEEE Transactions on Industry Applications 59.1 (2022): 70-80.   Sarkar, Soumyendu, et al. "Real-time Carbon Footprint Minimization in Sustainable Data Centers with Reinforcement Learning." NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning. 2023.

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

Interested applicants are welcome to contact Dr Wei Sun by email for pre-application enquiries (W.Sun@ed.ac.uk).

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in Electrical Engineering, Computer Science, Mathematics, or related areas. 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.

Further information and other funding options.

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. This project will also be supervised by Dr Cristian Sestito.

The project aims at building accelerators based on Field Programmable Gate Arrays (FPGAs) and suitable to deliver computer vision tasks through Generative AI. Generative Adversarial Networks (GANs) based on Convolutional Neural Networks (CNNs) are promising candidates in this direction: they exploit adversarial learning and feature extraction to execute a multitude of applications, including image dataset generation, image-to-image translation, face frontalisation. Specifically, the project targets deploying applications like this on FPGA-based Systems-on-Chip (SoCs) to be showcased in real-time systems, with an in-depth investigation on optimisation techniques to reach high throughput and low energy footprint (e.g., data quantisation and pruning). This will require preliminary training using software frameworks, like PyTorch or TensorFlow.

The required skills are as follows:

• Knowledge and expertise on FPGA design for AI using Verilog/VHDL (mandatory).
• Knowledge and expertise on training and testing CNNs using SW frameworks, like PyTorch or TensorFlow (mandatory).
• Basic knowledge on Systems-on-Chip based on FPGAs (desirable).
• Previous experience on using 3rd party IP cores for vision applications (desirable).
• Previous experience on training generative AI models (desirable).

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).

Further information and other funding options.

Two/three-dimensional flow-field parameters, such as species concentration, temperature and velocity are key to understand the physical and chemical behaviors of flows. The measurement data not only indicate the performance of the energy systems in many industrial sectors, such as aviation and manufacturing, but also provide experimental validation of the CFD models for R&D of the energy systems.

Laser absorption tomography (LAT) provides the unique capability of imaging thermophysical parameters of reactive flows, e.g., temperature, species concentration and velocity. LAT is implemented in a manner analogous to x-ray tomography, with the difference that, wavelength-specified incident laser beams are used rather than x-rays to acquire the absorption measurements, i.e., projection data in tomography. In industrial practice, the projection data can only be sparsely obtained from limited projection views, resulting in rank-deficient ill-posed inverse problem and thus leading to errors in image reconstructions.

This project will develop data assimilation for high-fidelity flow-field reconstruction using LAT. Data assimilation (DA) algorithms seek to solve the equations governing fluid motion subject to databased constraints. Instead of training an end-to-end neural network using simulated data, DA algorithms integrate ground-true physics, into the network, so call physics-informed neural network. Therefore, high-fidelity flow-field images are expected to be reconstructed by DA-assisted LAT. The student who joins our group will learn the fundamentals of laser absorption tomography, computational and data-driven solutions for inverse problems. The student will have a high chance of working with renowned international researchers and industrial collaborators.

Primary objectives:

1. Develop LAT model to integrate DA algorithms

2. Develop physical-informed neural networks for LAT and numerically test the network.

3. Design lab-scale experiment to validate the developed neural networks and LAT model.

4. Interpret flow-field behaviors from high-fidelity image reconstructions.

5. Improve communication and writing skills via conference presentations and journal publications.

Required skills:

1. Matlab/Python/Tensor-flow coding (mandatory)

2. Machine learning algorithms and coding (mandatory)

3. Tomography and inverse problems

4. Flow-field mechanics and dynamics

5. Optical/electronic experimental skills.

Please note that this advert will close as soon as a suitable candidate is found.

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 Electronic and Computer Science or Mechanical Engineering, 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.

Further information and other funding options.

Gaseous parameters, including species concentration, temperature and velocity are key indicators of the flow-field state and hence play a crucial role in characterizing the thermochemical performance of modern carbon-free power generation systems and advanced manufacturing processes. In many industrial applications, these parameters require to be measured online with high precision and rapidness, to effectively model and active control of the dynamic thermochemical processes.

Laser absorption spectroscopy (LAS) is a non-intrusive optical sensing technology for simultaneous measurement of gas concentration, temperature and velocity. The high-speed tunability of the semi-conductor laser diodes enables the rapid measurement up to MHz. However, complex signal processing of the spectroscopic data is still an issue limiting fast and online measurement. To date, efforts have been made by using machine learning algorithms to accelerate the spectroscopic signal processing. However, most rely on (a) lab-based experimental data that fails to consider the industrial relevant conditions and (b) complicated neural network models that are impractical to be deployed on light computing units.

This project will develop hardware-accelerated laser absorption spectroscopy technology and apply it to industrial gas measurement. The student who joins our group will learn the fundamentals of laser spectroscopic sensing and hardware acceleration techniques, and will be trained for carrying out experiments in both labs and real industrial conditions. The student will have a high chance of working with renowned international researchers and industrial collaborators.

Primary objectives:

1. Develop critical thinking of industrial relevant software and hardware implementation.

2. Develop self-supervised or unsupervised neural networks to process spectroscopic signals sampled under industrial conditions.

3. Deploy the developed neural network on light-weight edge computing platforms.

4. Carry out lab-based and industrial experiments to validate the developments.

5. Improve communication and writing skills via conference presentations and journal publications.

Required skills:

1. Matlab/Python coding (mandatory)

2. Time/frequency-domain signal processing (mandatory)

3. Verilog coding and testing

4. Machine learning algorithms and coding

5. optical/electronic experimental skills.

Please note that this advert will close as soon as a suitable candidate is found.

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 Electronical and Electrical Engineering or Mechanical Engineering, 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

Further information and other funding options.