Microwave Photonics filtering for 5G/6G and beyond spectrum bands.

This project will focus on Microwave Photonics developing experimental techniques for Radio over Fiber (RF) transmission systems where photonic filtering systems of RF Interference (RFI) will be developed at 5G/6G and beyond spectrum bands. The system under study will be focused on all-optical processing and controlling of RF signals for future communication systems using novel practical configuration schemes over a wide range of frequencies with high SFDR, low noise and suppression of high-order harmonics.  Novel techniques will be investigated to design and optimise transmission of radio signals over fibre optic modules with desired power consumption, and reduced complexity: Project supervisor Dr Shyqyri Haxha.  Project reference: SH1

Autonomous vehicle using LIDAR exploring security vulnerabilities

This project involves building an autonomous vehicle model which uses LIDAR for the purpose of exploring security vulnerabilities in the future automotive self-driving industry. Use Microwave photonic devices/systems to create disruption attempts, then propose and explore possible solutions from testing results. Furthermore, cyber-security in this industry will be explored by means of research and development regarding different technologies. Namely, keyless vehicles, vehicle networking and the modern CAN bus along with their weaknesses could be looked at in hopes of supporting advancements in security, by bringing together or possibly even creating new technologies:Project supervisor Dr Shyqyri Haxha. Project reference: SH2

Magnetic vortices in 3D systems

Nanoscale technology enables magnetic behaviour to be controlled using the shape of patterned elements.  Magnetic vortices are topological features present in circular elements, in which the magnetization rotates continuously within the structure plane, except for a central core that has perpendicular magnetization.  Oscillating vortices have the potential to be used as low power amplifiers, but this function is inhibited if the vortex core magnetization reverses during oscillation.  Using micromagnetic modelling, this PhD will explore how producing a 3D magnetic profile may improve vortex core stability and open up new applications for magnetic vortices. Project supervisor Dr Matthew Bryan. Project reference: MT1

Magnetization dynamics in 3D

Movement of domain walls – boundaries between regions of opposing magnetization – is a fundamental mechanism through which magnetization reverses direction.  Understanding domain wall behaviour is therefore essential for efficiently switching magnetic states with a device.  In planar nanostructures, domain walls have been proposed for several data processing, data storage and sensor applications.  3D fabrication promises opportunities to further expand the capabilities of magnetic devices, but it is unclear how the additional complexity of a 3D system changes magnetic reversal mechanisms.  This PhD will examine the role 3D geometries play in determining domain wall structure and motion. Project supervisor Dr Matthew Bryan. Project reference: MT2

HVDC a backbone for system resiliency

Following the war of currents, AC has been the technology of choice. Modern power systems include an increasing amount of DC technology, and play an important part in the energy transition. In the transmission system HVDC technology, be it back-to-back, links or multi-terminal networks are being deployed. This PhD investigates how HVDC can support the system, during some of the most challenging conditions, such as black-start and restoration, bad-data-injection as well as dynamic contingency planning. Project supervisor Dr Stefanie Kuenzel and Prof Stephen Wolthusen. Project reference: SK1.

Grid design and optimization including renewables, HVDC and storage

Power systems are undergoing a major transformation, with the large scale penetration of renewables, the need for energy storage and the HVDC as well as electric vehicle area. This grid transformation, entails a very large number of engineering and economic decisions, such as type and location of storage, optimized EV charging, type and location of renewables, location and topology of HVDC technology, where each choice made has a direct impact on the other choices. This optimization further has to work with the infrastructure already in place, guaranteeing optimal utilization. Project supervisor Dr Stefanie Kuenzel. Project reference: SK2.

Deep Learning for brain signals

Deep neural networks (DNNs) are one of the most popular and powerful machine learning systems used by companies such as Facebook and Google. It is therefore not a surprise that their successful applications have been in activities relevant to the cyber-world such as automated captioning of You Tube videos and facial recognition in Facebook images.  However, there is a lack of applications in exploiting the deep learning of neural networks for the analysis of biomedical data. In this direction, we have investigated how to reconstruct signals generated deep inside the brain from non-invasive measurements taken from the scalp. This is useful, as this circumvents the need to perform brain surgery to take a closer “picture” of the brain activities and can found applications in rehabilitation such as the prognosis of patients suffering from epilepsy. These brain signals called electroencephalogram (EEG) can also be analysed for other purposes such as the prediction of a person’s movement, the analysis/prognosis of diseases such as Parkinson’s disease and Schizophrenia, and monitoring of mental fatigue. Project supervisor Dr Cheong Took. Project reference: CT1

New Methods and Theories for 3D and 4D Signal Processing

Traditional signal processing and machine learning applications rely on the use of learning methods in the real- and complex-valued domains. However, modern technologies have fueled an ever-increasing number of emerging applications in which signals relies on unconventional algebraic structures (e.g., non-commutative). In this context, advanced complex- and hypercomplex-valued signal processing encompasses many of these challenging areas. In the complex domain, the augmented statistics have been found to be very effective in different methods of machine learning and nonlinear signal processing. However, processing signals in hypercomplex domains enables us to exploit some different properties, albeit raising challenges in designing and implementing new and more effective learning algorithms. More generally, learning in the hypercomplex domain allows us to process multidimensional data as a single entity rather than modelling as a multichannel entity, hence preserving the integrity of the data. In that direction, quaternions have attracted attention in the signal processing and machine learning communities for their capability of dealing with 3D and 4D models, thus providing an exciting area to propose new methodologies in signal processing. Project supervisor Dr Cheong Took. Project reference: CT2

Neuromorphic MPSoC for Olfaction

The recent breakthrough in designing customized neuromorphic chips capable to compute complex neural algorithms and the better understanding of the biological olfactory mechanisms offer the perfect starting point for investigating new neuromorphic algorithms for reconfigurable software-hardware heterogenous platforms with applications in artificial olfaction. The research will explore computational models capable to deploy many parallel computing cores in low cost heterogeneous multiprocessor system on chip platforms and cope with the smell recognition challenges that arise in the very short transitional olfaction signals measurements done in noisy environmental conditions.  The project seeks to achieve ultra-rapid olfaction measurements with possible applications in health monitoring and 3D plume odorant mapping. Project supervisor Dr Alin Tisan. Project reference: AT1

Autonomous non-invasive indoor abnormalities detector for elderly occupants

The increasing of elderly population requires a rise in the provision of specialised support services. This provision is putting existing resources under strain as the support requirements for the elderly grow. The dramatic change in human interaction and behaviour triggered by the recent pandemic outbreak has imposed general social distancing, forcing seniors to stay in their own homes whenever possible. The research will explore the development of an end-to-end system, based on a distributed computing paradigm for non-invasive detection of abnormal ambient phenomena (characterised by acoustic, olfaction and electromagnetic signals) to provide an effective care monitoring and decision-making service for domestic environments. Project supervisor Dr Alin Tisan. Project reference: AT2

The PhD (Doctor of Philosophy) degree is a four year full-time or eight year part-time programme of research on a topic that is linked to one of the Department’s research groups under the supervision of one of our academic staff who is a specialist in the research field.  The core one-word requirement of a PhD is ‘novelty’; there has to be something within a PhD that can be deemed to be novel.  This has to be demonstrated by reviewing the research literature mainly during the first 9 months to establish a specific area of interest where there is scope for moving knowledge forwards.  A way of exploring that area formally is established, for example through experimentation, and this phase lasts generally into the third year.  The writing of the thesis will usually start during the last 3-6 months of the third year.

The MPhil (Master of Philosophy) degree is a three year full-time or six year part-time programme of research on a topic that is linked to one of the Department’s research groups under the supervision of one of our academic staff who is a specialist in the research field.  It differs from the PhD (see above) in that there is no expectation of ‘novelty’.  Rather, the MPhil degree is based around excellent research that furthers knowledge but that additional knowledge does not have to be novel.  This has to be demonstrated by reviewing the research literature mainly during the first 6 months to establish a specific area of interest where there is scope for moving knowledge forwards. A way of exploring that area formally is established, for example through experimentation, and this phase lasts generally well into the second year.  The writing of the thesis generally starts during the last 3-6 months of the second year.

The MSc by Research is a full-time one year research degree working on a topic that is negotiated with a member of staff during the application stage.  This topic will be in an area that fits well within one of the Department’s research groups which are shown here.  There are no formal taught modules and the MSc by Research is assessed on the research work itself.  Regular supervisions will underpin the research and there will be opportunities to present progress on the research to internal Research Seminars as well as conferences depending on the area of work and progress.  At the start of the programme, there will be introductory activities on research skills, giving presentations, writing reports and laboratory safety.

Students discuss their research skills training needs with their supervisor and adviser soon after they start their research degree.  Further information and guidance can be found online here.  The University's Researcher Development Programme offers a series of transferable skills courses designed for research students to help them to develop their skills alongside their research.  Some of these courses are specifically designed to help students complete their research degree, while others help develop more general skills and enhance their employability.

Students on the MSc by Research programme are able to attend any of the 3rd or 4th year modules taught within the Department’s MEng programme as desired to support their research (please note that these are not assessed as part of the MSc by Research). 

Studentships are available from several sources, which may vary, from time to time, in terms of eligibility and application procedures. The University has information on PhD funding available here:

Applications are invited in specific areas that will enhance our Department’s research capabilities. Please contact the relevant supervisor to discuss the possibility of submitting an application. 

All PhD positions (whether self-funded or not) may be applied for only after consultation with a proposed supervisor.  That is, before you make an application you will need to find a supervisor by contacting a member of academic staff (with research interests similar to yours), to discuss your proposal.  If you find a member of academic staff who is prepared to supervise you, then they will be able to answer any questions that you have and help you with your application.

Applicants should ideally hold, or be predicted to achieve, an undergraduate degree or equivalent in Science/Engineering (minimum requirement: 2.1) and meet the University’s English Language requirements (for international students). An MSc in relevant disciplines is desirable but not essential.

Please consult with your potential supervisor for advice on whether or not the subject of your degree is acceptable.

Take a look at the project you are interested in and read the profile of the associated supervisor. If you are interested in applying, please contact the supervisor shown and find out whether they would be prepared to supervise you. You can then start to carve out your specific ‘research plan’ with advice from your potential supervisor. Applications submitted without the agreement of the potential supervisor are unlikely to be considered. Each supervisor may have further or specific requirements for applicants. 

Your application must be submitted through the University online application system, for which you need to register at the outset of the process. The online application system provides a lot of detail, including a list of documents that are required for each PhD application. You will need to nominate two referees (neither of which should be your proposed supervisor) on this online form. They will be contacted by the University directly, but it is always a good idea for you to contact referees before you submit your application to make them aware of your plans, and so they can respond to the University request swiftly.

Please provide:

• an up-to-date CV;
• all degree transcripts (plus certificate if available) and copies of any other qualifications (in English);
• copies of English language qualifications (if English is not your first language);
• contact details for two references;
• a research statement/proposal; and
• a financial plan. You will also be required to have considered (with the help of your intended supervisor) how any specific costs associated with the research project (such as participant payments, travel costs or other equipment costs) will be met during the study period.

Once you have carefully checked your application press the submit button. Your application is on its way!