Opportunity in Engineering Department: Durham Doctoral Teaching Fellow in Electrical or Electronic Engineering

Durham Doctoral Teaching Fellow in Electrical or Electronic Engineering

Applications are invited for a Durham Doctoral Teaching Fellow (DDTF) in Electrical or Electronic Engineering. The fellowship aims to support the completion of a PhD degree while at the same time acquiring teaching skills that will enhance the Fellow’s career progression.

Start date: 1 October 2024

Application closing date: 13 September 2024, 12 noon

 

The Department

The Department of Engineering at Durham University is continuing a period of strategic growth, and this is an exciting opportunity to join an expanding, unified Engineering Department which is recognised as one of the very best in the UK, with a reputation for excellence in teaching, research and employability of our graduates. In the REF2021 exercise, 95% of our Engineering outputs were graded 3* or 4*. The Department is committed to an ethos of research-led education at all levels of our taught programmes. Engineering at Durham University is ranked 5th in The Guardian University Guide 2024, 7th in The Times and Sunday Times Good University Guide 2024 and 5th in The Complete University Guide 2024. We were ranked 2nd in Engineering behind only Cambridge in a recent Institute for Fiscal Studies report on the difference in graduate earnings by UK university choice.  We are a scholarly community that is open, representative and diverse: our commitment to this is expressed through work of the Department’s Equality, Diversity and Inclusion Group, and our Athena Swan Silver award.

The Department of Engineering demonstrates significant depth of research excellence across a range of disciplines, with activities that are currently concentrated around three Research Challenge areas: Sustainable Infrastructure, Future Energy Systems and Advanced Materials, Electronics and Communications. Thematically, our research is split into eight Research Nodes which group together our day-to-day activities and expertise. This DDTF would be likely to join the Advanced Materials and Electronic Devices, Communication and THz, or Electrical Power nodes. We are particularly proud of the collaborative interdisciplinary nature of our research, which also includes strong industrial partnerships.  For more information, please visit our Department pages here. 

The Role

The purpose of the DDTF is to support the Fellow in completing a high-quality PhD thesis while at the same time acquiring teaching skills that will enhance the Fellow’s career progression.  In addition to conducting a high-quality research project, the DDTF will assist and support faculty staff to deliver teaching and support learning. The role aims to enhance the Fellow’s own progression and achievements, both with respect to research and teaching, whilst providing stable and supported employment with the University.

The Fellow will work under the supervision of academic faculty in the Engineering Department and will be assigned a teaching mentor (normally a single member of academic staff) who will provide oversight, supervision and support for all the teaching-related activities.  The teaching mentor will normally be distinct from the supervisor of the research project. 

Teaching responsibilities will be typical of the range of teaching activities appropriate to the discipline and may include

• Leading seminars and tutorials.
• Demonstrating in practical laboratories.
• Providing individual guidance to students on course materials and study skills.
• Providing feedback on progress to students.
• Assessment of work based on agreed criteria.
• Programme administration.

In the latter part of their teaching position, a Fellow’s responsibilities may include

• Co-teaching a module with a member of academic staff who has primary responsibility for the module.
• Supporting a member of academic staff in revising or updating module materials.
• Co-developing new laboratory experiments.

The role of Durham Doctoral Teaching Fellow provides the opportunity to conduct a high-quality research project and to contribute to the delivery of outstanding teaching, working with the support and under the direction of more senior colleagues. The post will involve a significant teaching load during term-time and the activities may extend into the summer period. However, the schedule of teaching activities will be designed with the requirements of the PhD project in mind.  There will be an expectation that the Fellows secure a formal teaching qualification leading to Associate Fellowship of the Higher Education Academy, and they will be provided with the time and support required to achieve this qualification.

 

Key responsibilities:

• To conduct a high-quality PhD research project in collaboration with the supervisor.
• To undertake duties relating to teaching, administration and citizenship as required by senior colleagues and appropriate to your grade of employment.
• Teach existing modules/sessions as required, demonstrating an understanding of the subject materials and the use and value of appropriate learning The area of teaching will be coherent with the DDTF’s experience.
• Manage own teaching and design, plan and produce teaching materials for a variety of delivery methods including online and blended delivery.
• Build an awareness of different approaches to and methods of teaching and learning support.
• Take on board feedback on teaching and engage with others in continuing professional development.
• Set student assessments, assess students’ academic work and provide feedback throughout the year.
• Deal with students’ queries about the content and delivery of a module.
• Build internal contacts and participate in networks to exchange information.
• Make an active contribution to an inclusive community in which diversity is embraced and celebrated.
• To contribute to fostering a collegial and respectful working environment which is inclusive and welcoming and where everyone is treated fairly with dignity and respect.
• To engage in wider citizenship to support the department and wider discipline.

 

Person Specification

Candidates must outline their experience, skills and achievements to date which demonstrate that they meet or that they have the potential to achieve the essential criteria. 

Essential Criteria

• Eligible to work in the UK.
• Home Fee Status Students Only.
• A first or good upper second-class degree or Masters in Electrical or Electronic Engineering or a related subject.
• Demonstrable ability to work cooperatively as part of a team, including participating in research meetings.
• Good oral and written communication skills.
• Ability to explain complex concepts at a level appropriate to the audience.
• Ability to engage enthusiastically with material and to adopt appropriate styles and methods to achieve the desired learning outcomes.

Desirable Criteria

• Experience of teaching and assessment with evidence of positive student feedback.

 

Funding Notes

This Durham Doctoral Teaching Fellow (DDTF) position will last for 54 months and comprise 42 months of research and 12 months of teaching (1540 hours in total, based on a full time, nominally 35 h per week 35-hour week and a 44-week year). The Durham Doctoral Teaching Fellow (DDTF) will be entitled to a total of 42 months of stipend (UKRI value) and 12 months of salary at Grade 6 (£28,929) during the duration of the fellowship. Annual leave is 27 days, plus four Customary days, plus eight Public Holidays. During months 1 to 48, the DDTF will commit 25% of their time to teaching activities, while the last 6 months are dedicated entirely to research.

 

Contact Information

• Dr Behzad Kazemtabrizi, Director of Education, [email protected]
• Professor Charles Augarde, Head of Department, [email protected]
  

 

How to apply

You will need to select a project from the list provided at the end of this page. You can only choose one project. You can find the project’s lead supervisor in the title.

All applicants are asked to submit:

1. A CV (maximum two pages of A4).
2. A Personal Statement explain why you are interested in this position and what interests you in your chosen project (maximum one page of A4).
3. Qualification transcripts.
4. Two referees’ contact detail.

Save all application documents with your name and document type as PDF files.

Submit the formal application via University’s online application form. While completing the form, in the “Field of Study” box please type “DDTF” followed by the title of your chosen project. Choose “No” when asked “Have you been in contact with a potential supervisor”. In the “Scholarship details” choose “Other” and the 1^st of October as the starting date. This will ensure that your application is processed promptly.

 

Application Deadline

Sunday 11 August 2024.

 

Available Projects

1. Dielectric properties of patterned transparent conductive oxides (Dr Iddo Amit)

Sub-wavelength periodically patterned scattering structures can interact with incident wavelengths, changing their polarisation, amplitude or phase. Their ability to enhance certain frequencies whilst quenching others is of specific interest for use as filters and lenses. Ebbesen’s seminal work [T.W. Ebbesen et al., Nature 391, 667 (1998)] introduced the concept of perforated metallic films as a way to preferentially enhance transmission of specific wavelength, as a function of unit-cell symmetry and dimensions. Their subsequent works, and those of others, enhanced the versatility of these surfaces, but not their tuneability. Recently, interest in the properties of semiconducting patterned surfaces has been rekindled with application in visible light communication and quantum technology.

This research project will apply Ebbesen’s methodology to polycrystalline transparent conductive oxides (p-TCOs) with the aim of creating in-situ tuneable, and multifunctional optical surfaces that can double as electrical contacts and optical modulators for solar cell applications. The choice of p-TCO provides two new degrees of freedom in tailoring the surfaces to their unique application, a choice of material morphology, such as grain size and doping level, that will determine the fundamental electromagnetic properties, and the ability to in-situ tune the surface by the application of external fields or surface currents.

 

2. Finding out how new PV technologies work in real-world conditions (Prof. Chris Groves)

New solar technologies based on materials other than conventional silicon may revolutionise the way in which we capture solar power.  However, whilst these technologies are developing rapidly in labs, there is less understanding of how they will perform in real conditions – meaning that we cannot accurately forecast the power they will provide, nor optimise the devices for real world operation. 

This project seeks to change this by developing new approaches to simulate the performance of new photovoltaic (PV) devices.  We will focus on how the Sun’s energy is reduced and shaped by the atmosphere, pollutants and cloud on its route to ground level, and how this diversity of photons is absorbed in the semiconductor material.  Working with leading drift diffusion modelling techniques, we will use this new capability to map the energy generation capabilities of emerging PV devices around the world, and identify opportunities for this technology to differentiate itself from conventional Silicon.

 

3. AI-Driven Discovery of Next-Generation Electronic Materials for Rechargeable Batteries (Dr Mehdi Keshavarz Hedayati)

The advancement of battery technology is essential for the transition to sustainable energy solutions. Batteries play a crucial role in the global energy system, particularly in the transport and power sectors, where they are pivotal for the deployment of electric vehicles and renewable energy storage systems. In 2023, battery storage capacity was the fastest-growing commercial energy technology, reaching nearly 42 gigawatts. Despite this progress, the development of high-performance, durable, and safe battery materials remains a significant challenge due to the rising demand for more efficient and sustainable energy storage solutions.

This PhD project proposal aims to address these challenges by leveraging Artificial Intelligence (AI) and quantum simulations to accelerate the discovery and optimization of new electronic materials for batteries. Quantum chemistry simulations are instrumental in predicting the properties of new materials, such as conductivity, stability, and reactivity, which are critical for battery performance. By simulating various materials at the quantum level, the project aims to screen many potential candidates, identifying the most promising ones for further experimental testing.

The research objectives of this project include developing AI models, utilizing quantum simulations, and conducting high-throughput screening. Specifically, the project will focus on creating and refining AI algorithms to predict the properties and performance of potential battery materials.

 

4. Integrating Wave Energy with Offshore Wind Farms (Prof. Alton Horsfall)

The spatial spread of wave energy systems and the high peak, low average power generated are significant challenges for the power electronic systems designed to integrate this energy source with the distribution grid.  One possibility is to integrate wave energy with offshore wind infrastructure, however this requires significant enhancement of the output voltage in to the multiple-kV region and the synchronisation of the generated power with the wind turbine, without excessive weight. 

The project is to consider the integration of a novel high gain DC-DC converter with the collection network for an offshore wind farm.  This will include the simulation of the collection network, development of experimental hardware and control algorithms to support the realisation of fully integrated wind – wave systems.

 

5. Optimum operations planning for energy systems with large-scale distributed solar PV using novel artificial intelligence methods (Dr Behzad Kazemtabrizi)

This project will aim at maximising the integration of solar photovoltaics (PV) generation capacity into the UK electricity grid as large-scale distributed generation, at the distribution and consumer voltage levels, focusing on both existing and novel PV devices. Using innovative operations planning and energy management tools developed in this project, distributed solar PV generation can then be deployed nationally working in tandem with central offshore wind energy generation as a safe and reliable way to decarbonise our energy system without compromising the operational security (i.e., the ability of the system to maintain stable operation following disturbances) and without reliance on gas generators to alleviate impact of abnormal operating conditions.

The project will focus on (i) developing the next generation computational decision support tools for whole-system forward optimum operations planning (i.e., planning the operation of network assets in advance of real-time for example for day-ahead timespans), using a forecast model which tracks both spatial and temporal availability of the solar irradiation for specific regions of interest , (ii) using novel artificial intelligence methods such as physics-informed learning to develop representative models that are able to accurately characterise the physical and operational constraints of a variety of solar PV devices ranging from existing commercial models to more novel high capacity-factor variants that make use of new semiconductor materials, and (iii) studying the impact of large-scale deployment of distributed solar PVs into the UK electricity network both in terms of economics and security and resiliency of operation.

 

6. Using satellite observations to assess the impact of natural disasters on renewable energy supply (Dr Roderick MacKenzie)

As the world transitions to net zero, our reliance on renewable energy sources will substantially grow. Unlike fossil fuels, that can be mined and stored, renewable energy must be generated close to the time of use. Currently, almost all renewable energy (wind/solar) is consumed immediately after production, with only a small fraction being stored. This PhD project will focus on examining how large-scale natural disasters, such as volcanic eruptions, impact future energy grids powered predominantly by renewable energy sources.

Using a combination of satellite data and advanced computer modeling, this research project will investigate how past natural events have disrupted solar energy generation and the subsequent effects on local and national electricity grids. Building on this historical understanding, the project aims to assess the resilience of future grids, where low-carbon generation will constitute a much larger share of energy production.

Join us in this cutting-edge research to contribute to the development of robust, sustainable energy systems for a net-zero future.

 

7. Propagation modelling for the THz band (Prof. Sana Salous)

Future mobile radio applications such as holographic application and remote surgery are expected to require high bandwidths which are not available in the lower frequency bands. The communications and THz group at Durham University has extensive facilities in the sub-THz band (100-320 GHz) and in the upper THz band (750-1100 GHz). The proposed research topic will make use of the facilities to conduct measurements in typical deployment scenarios such as point to point and point to multi-point. The data will be processed to estimate the channel parameters and to develop appropriate channel models.

Durham University is a member of the international standards which include ETSI (European Telecommunications Standards Institute) and the ITU (International Telecommunication Union).  The successful candidate will be expected to generate results appropriate for contributions to the standards.

 

8. Noise-Vibration-Harness (NVH) Improvement of Permanent Magnet Synchronous Machines for Electrical Vehicles (Dr Nur Sarma)

Electric vehicles, particularly electric cars, are increasingly becoming attractive due to their positive impact on the environment, climate change, as well as air quality, lower running and maintenance costs, and wide range of model selection. According to Statista, more than 1,000,000 fully electric cars are now on UK roads, and electric cars accounted for 15.2% of new car registrations in March 2024. This number is expected to exceed 9 million by 2030, accounting for 18% of total car sales for the year, meaning nearly one in five new cars sold is expected to be electric.

The critical component of an electric vehicle's drivetrain is an electric machine, which converts electrical energy from the traction battery pack into kinetic energy or mechanical motion and creates the driving force propelling the vehicle forward. Permanent magnet synchronous machines (PMSMs) are the most employed electric machine types used in electric vehicles due to their high power, torque, and efficiency. However, noise vibration and harshness (NVH) represent important issues that must be overcome in designing and developing new, lightweight, and more efficient PMSMs for electric vehicles. For example, the development of PMSMs with enhanced acoustic performance is significantly crucial in not only creating a better driving experience but also improving the overall performance and durability of the electric machine. Therefore, understanding noise emission at the early design stage of an electric machine through simulations and analysis is significantly important.

This PhD project aims to develop a novel optimization tool to propose the best NVH reduction methods for improving the NVH features of PMSMs used in electric vehicles, which positively impacts overall performance and efficiency. This novel optimization tool will be capable of understanding and evaluating the unique design aspect of PMSMs, such as the number of poles, used magnet types, slot numbers, etc., and will propose the most efficient methods to improve the NVH performance of any unique PMSM design used in electric vehicles.

 

9. Artificial Intelligence for Resilient Power Grids with Distributed Energy (Dr Mahmoud Shahbazi)

The increasing adoption of renewable energy sources like wind and solar brings both environmental benefits and operational challenges to power grids. Their intermittent nature can destabilise the system if not managed effectively. This research proposes a novel approach that leverages Artificial Intelligence methods, such as Machine Learning (ML), to optimise the integration of distributed energy resources (DERs) and enhance overall grid resilience.

Traditional power grids rely on centralised generation and control. However, the growing penetration of DERs, including rooftop solar panels and battery storage, introduces variability and uncertainty into the system. This proposal aims to address these challenges by utilising ML algorithms to: (i) Predict DER power generation: Develop models to forecast wind and solar power output, allowing for proactive grid management strategies. (ii) Optimize DER dispatch: Employ ML to determine the optimal power output and storage levels of DERs in real-time, ensuring grid stability. (iii) Identify and mitigate grid anomalies: Leverage ML for anomaly detection, enabling the prediction and prevention of potential grid failures.

The research will adopt a data-driven approach, combining power system engineering expertise and machine learning techniques. Collaboration with utilities and DER operators will be crucial to build a comprehensive data repository encompassing grid data, weather forecasts, and DER information. This data will be used to train and validate various ML models, including time series forecasting for generation prediction, reinforcement learning for optimal dispatch, and anomaly detection algorithms. The ultimate goal is to develop an ML-based framework that can be integrated into existing grid management systems.

 

10. Barcode of the Future: Pioneering Sustainable IoT with RFID Innovation (Dr Roy Simorangkir)

The Internet of Things (IoT) is revolutionising multiple sectors such as logistics, healthcare, and retail with enhanced monitoring and data collection. At its core is Radio Frequency IDentification (RFID) technology, which uses Electro-Magnetic waves (EM) to identify objects through a system of reader and tag affixed to the target object. However, IoT’s reliance on electronic components raises environmental concerns. For instance, semiconductor-based chips—the ‘brains’ of RFID tags—and the tag themselves both contribute to energy-intensive production, resource depletion, and escalating e-waste, along with high production costs. The Global E-waste Monitor projects e-waste generation to reach 74.7 million metric tonnes by 2030. Based on the same report, UK, ranking among the top 10 e-waste generators, produced 1.598 kilo tonnes (kt) of e-waste in 2019. In response to these challenges, Chipless RFID (CRFID) technology has emerged as a greener and more cost-effective solution. Costing comparably to barcodes, CRFID paves the way for broader adoption and scalability.

This project aims to synergise state-of-the-art CRFID technology with eco-friendly materials and artificial intelligence (AI) advancements to create smart, barcode-like RFID systems that will embody simplicity and universal applicability while enabling complex data encoding. Imagine a barcode-like solution that not only identifies an item but also tracks its critical parameters, including temperature, humidity, or other metrics, offering a comprehensive snapshot of its status. This innovation will transform IoT while tackling critical environmental challenges.

 

11. Autonomous UAV fleet deployment for real-time water quality monitoring (Dr Oliver Vogt)

With the upcoming legislative changes requiring much more frequent monitoring, water suppliers in the UK are attempting to automate the activity. Hence, this PhD project aims to design, program and implement a pilot solution for Northumbrian Water https://www.nwl.co.uk/ from its Hordon site in County Durham. Further industry partners involved are RS Hydro (https://www.rshydro.co.uk/ Lab on a Chip), Makuto (https://makutu.io/ IoT Data Infrastructure), and SkyPorts (https://skyportsdroneservices.com/ Drone Infrastructure).

Building on already taught content as well as available UAV hardware at the Engineering Department of Durham University, the project will design, develop and implement a pilot solution for an autonomous UAV fleet based on the https://ardupilot.org/ platform.

Major work packages will require intensive collaboration with the above industry partners: (i) Design of a UAV suitable for autonomous water quality monitoring (with Northumbrian Water). (ii) Integration and weight reduction of the Lab on the Chip sonde (RS Hydro). The water quality parameters will be instantly available, but closer integration with the onboard flight electronics has great potential to increase flight time due to reduced weight. (iii) The Ardupilot platform will also be used to develop software and hardware for the UAV fleet with industry partner SkyPorts. (iv) To carry out safe missions, it will be imperative to integrate AI object detection into the operation of the UAV - in close collaboration with IoT partner Makutu.

 

12. Development of a novel photodetector for hyperspectral imaging (Prof. Dagou Zeze)

A hyperspectral image is a composite image that provides information from different parts of the electromagnetic spectrum (e.g., a composite NIR and visible image). These images are particularly useful when visibility is compromised by smoke or bad weather conditions, or when medical conditions, such as abnormalities, manifest themselves clearly outside the visible range.

The research will exploit the piezophototronic effect, whereby both mechanical stress and incident light modulate charge transport characteristics in piezoelectric semiconductors to study charge transport in photoexcited nanowire-polymer blends. The knowledge gained will be utilised to develop a hyperspectral photodetector with a dual functionality: imaging applications and pressure sensing for the emergency services and for the healthcare industry. To achieve hyperspectral imaging, the photodetector will operate under mechanical stress to modulate the absorption efficiency of certain wavelengths by tuning the Schottky barrier height, effectively filtering them out. As the stress is altered controllably, the high-efficiency absorption band shifts to provide a path to measure light in different spectral bands. Additionally, the use of an anisotropic composite, an array of aligned semiconducting nanowires in a polymer matrix, will facilitate the creation of a light-polarization-sensitive photodetector. A careful selection of the host matrix and growth conditions makes it possible to develop a low-cost device that is capable of modulating charge under mechanical stress and illumination. The project aims to deliver a prototype demonstrator.