Opportunities available at Nottingham Trent University

Below are the details for two projects at Nottingham Trent University, which Sundance are on the Advisory Board for, two fully funded PhD placements and a position working as a part time lecturer and part time PhD study. Candidates can attain more information by contacting Pedro Machado – pedro.machado@ntu.ac.uk

Project: Underwater Plastic Detection


Unit(s) of assessment: Computer Science and Informatics

Research theme: Computational Intelligence and Applications Research Group (CIA)

School: School of Science and Technology

This video shows plastic and crayfish being detected. YOLOv5 nano is being used to perform classification in real-time. It can be seen that the model is robust and capable of detecting both plastic and crayfish in low-visibility conditions


Plastics thrown away by humans are normally transported to the oceans by rivers. It is not clear how much plastic is transported every year from land to seas because of lack of metrics and standard monitorisation techniques. One of the approaches is on the monitorisation of plastics floating on the water surface. Only a small part of the plastic waste in rivers and oceans floats on the surface, the rest sinks to deeper waters or to rivers/oceans floor, threatening the local flora and fauna. So far, there is no way of detecting plastic at the bottom of rivers/oceans on a large scale. Traditional monitoring methods, in which divers manually collect image data along lines or taut cords (so-called transects) only allow for assertions about very limited areas. In addition, these methods are highly time-consuming, expensive and with very limited results. Generally, such methods neither provide georeferenced data that can be used to find locations again, for example to recover plastic or check its condition.

In this research project, we propose a non-evasive approach where an underwater drone, funded by the department back in July 2021, is used to collect visual data that will be georeferenced to assess how macro-plastics are transported underwater, how much of that plastic is deposited in the bottom of the Trent River and how those plastics affect the ecosystem. The multi-disciplinary team involved in this project will analyse data from different perspectives and use the findings to prepare and submit a research proposal to BBSRC/UKRI to further explore and create understanding how much plastic is carried underwater, how much of the plastic deposits on the Trent’s River soil and how it affects the ecosystem. This research project aims to develop a proof-of-concept validator. The project, if funded, will allow to prepare the existing underwater drone to systematically collect and label data to be post-processed by the Academic staff. A research assistant will support the academic staff to collect the initial datasets, to be analysed and generate the initial results that will be used to prepare a stronger proposal to be submitted to BBSRC/UKRI thematic calls on underwater plastic detection.



To develop a programme to:

  • Design and implement a systematic approach for collect and label visual datasets using an underwater drone.
  • Process the dataset using ML/AI/statistical analysis algorithms to understand how plastic is transported underwater, deposit in the soil at the bottom of the Trent River and how it affects the ecosystem.



  • Design and implement a procedure to systematically collect and label datasets.
  • Collect the initial datasets to be processed using ML/AI/statistical analysis algorithms
  • Demonstrate the dataset collection using an underwater drone.



Plastic prediction validation batch 1

Plastic prediction validation batch 2

Confusion matrix

Metric results

Project: Internet of Water (IoW)


Unit(s) of assessment: Computer Science and Informatics

Research theme: Computational Intelligence and Applications Research Group (CIA)

School: School of Science and Technology


The Internet of Water (IoW) aims to lead the Transformation of the UK aquatic species monitoring, using digital technologies. The latter will be built upon the work of the UPD project, which enhances detection/visualisation of pollution agents, as well as real-time monitoring of endangered and invasive aquatic species; plants/animals. This project aims at extending the UPD project for monitoring underwater invasive and endangered species. It will use a human-in-the-loop model that constantly focuses on mixing human knowledge and expertise with digital information. Furthermore, the research will use the research outcomes to be successful in future applications for external funding and development of impact case studies which will contribute to the REF2028.


1. Create new knowledge to extend the scope to further support aquatic ecosystem.

2. Develop 2 x intelligent underwater sensor prototypes for compensating the low visibility underwater by combining Stereo Vision Cameras/LiDAR/acoustic systems and edge computing platforms.

3. Creation of a proof of concept of an IoW ecosystem that will cover the interoperability across data, services, machine-to-machine communication.

4. Develop a solution to transform using an array of digital technologies including Internet of Things, Big Data and Artificial Intelligence.

5. Disseminate project outcomes in two articles to be submitted to high-impact journals [IEEE transactions on Internet of things and Elsevier Applied Soft Computing Journal].

6. Develop and integrate existing complementary tools to be utilised for enhancing the understanding of underwater ecosystems.



Advisory board:

Prof Rob Morris (NTU) – Research focused on ultrasound imaging.

Prof Rachel Stubbington (NTU) – Research is focused on River Ecology

Prof Andrew Hirst (NTU) – Research is focused on ecology, physiology, and impacts of climate warming, with special emphasis on aquatic organisms.

Dr Erika Whiteford (NTU) – Her research is focused on terrestrial and freshwater ecology, in particular investigating biogeochemical cycling and nutrient stoichiometry of low nutrient systems in the Arctic.

Mr Matt Easter (CEO of the Trent River Trust) – The TRT has several projects across the river Trent.

Mr Flemming Christensen (CEO of Sundance Multiprocessor Technology Ltd.) – Expertise on Heterogeneous Computing and Vision Systems.

Elevating Internet of Things Devices for Underwater Communication Applications Employing Highly-Efficient Artificial Cognitive Devices.

School: School of Science and Technology
Study mode(s): Full-time / Part-time
Starting: 2023
Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded

NTU’s Fully-funded PhD Studentship Scheme 2023

Project ID: S&T18

The last few decades have seen an extraordinary acceleration of biodiversity change, both in terms of severity and speed. To counter the negative impact of the negative forces that impact the marine ecosystem, it is necessary to measure the contributing factors and communicating results in real time to be able to take immediate and necessary actions. Whilst measuring various factor is feasible using the latest sensor technologies, most underwater communication networks share an underlying deficiency. It is hard if not impossible to communicate directly across two mediums, i.e. water-air due to the water-air barrier. Deeply submerged sensors are unable to interact immediately with nodes that are on the water surface. This limitation is due to inherent properties of wireless signals in different medium. Current viable solutions include utilising wires to transmit the signal to the surface to an antenna or a processing unit that includes embedded computing platforms. New emerging wireless crossmedia communication are becoming available, which have a drawback. This is a low-bandwidth and high signal attenuation. An efficient communication protocol to counteract the inherent deficiencies of these new devices. The PhD research programme focuses on developing highly-efficient Artificial Cognitive Computing devices for advanced monitoring and behavioural analysis of underwater species. Such devices will combine lossless data compression techniques with state-of-the-art Deep Learning Networks (DLNs) algorithms (e.g. YOLOv7, MobileNetv3, EfficientNetv3) running on heterogeneous edge devices (HEDs). HED are specialised embedded computing devices equipped with Processor Systems connected via very high-speed internal buses connected to parallel computing devices (such as Graphical Processor Units, Field-Programmable Gate Arrays and Tensor Processor Units). The highly efficient DLNs will be used to process, on the HED, the data being collected from a wide range of sensors (such as vision, acoustic and LIDAR) and the output will be in the form of explainable AI. Furthermore, the project will also explore the use of wireless/optical underwater communications technologies to enable a means of data exchange for underwater (IoT) sensors. The project will also utilise the IoT sensors network technology that is being developed as part of an ongoing Internet of Water (IoW) project in our research group. The IoW focuses on the creation of technology to improve sustainability through real-time Nottingham Trent University 2 monitoring and behavioural analysis of ecosystems towards the detection and visualisation of pollution agents and tracking of populations of aquatic species. This PhD research programme will transform the IoW sensors into highly efficient artificial cognitive devices.

Advancing Computational Intelligence to Enhance Health and Well-Being Provisions.

School: School of Science and Technology
Study mode(s): Full-time / Part-time
Starting: 2023
Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded

NTU’s Fully-funded PhD Studentship Scheme 2023

Project ID: S&T16

The multi-sensor data collection capabilities of smart sensing devices are improving healthcare. They enable the generation of rich contextual data to monitor the behavioural patterns and other contextual information that unfold in everyday settings. Wearables and smartphones can wirelessly and non-invasively monitor vital signs (including heart rate, skin temperature, etc) as biomarkers that reflect associated human behavioural/activities. Routinely and continuously monitoring these signals is crucial for maintaining the health and well-being of individuals including vulnerable groups whose mental and physical conditions are volatile (such as autistic syndrome, depression, anxiety, and elderly). While Artificial Intelligence/Machine Learning (AI/ML) has been successful in tasks such as image processing, new methods will be needed to deal with user-generated digital biomarkers collected via sensing devices which by nature contain significant noises. This is important in the face of challenges presented in the determination and establishment of the link between physiological biomarkers and health/well-being which is a critical yet necessary objective. Today, the question is no longer about the need to adopt technology in health care settings but the choice of the technology that fit the appropriate context that provides desired outcomes. Consequently, the following challenges remain, extending existing multilevel and multivariate sensor fusion algorithms to provide a robust and complete data description of behavioural patterns; extending the state-ofart AI/ML time series-based algorithms (e.g. XGBoost, Deep Learning – LSTM, CNN etc.) and develop novel explainable AI/ML algorithms to meet the desired outcomes; new methodologies and computational intelligent platforms including web/mobile applications to provide health and mental well-being interventions. This PhD Research project will focus on the application of non-invasive sensing devices to predict and monitor the behavioural patterns/ health/wellness/mood specifically stress, depression, anxiety-related conditions of individuals including vulnerable groups. A range of sensing devices including wearables will be investigated to understand their efficacy and reliability in monitoring vital signs. It will also focus on extending existing multilevel, multivariate sensor fusion, algorithms, and AI/ML algorithms and how they can be integrated into intelligent healthcare Nottingham Trent University 2 systems to improve existing solutions to enhance the life experience of the targeted groups. Specifically, this research will examine and analyse data gathered from the sensor network in conjunction with the prevailing observation of the participating users’ behaviours whilst exploring the use of standard stress/anxiety/depression self-assessment tests. This will lead to the development of correlation maps that links the signal to the aforementioned behavioural patterns, setting the scene to inform intervention and care.

Supervisory Team:
Dr Isibor Kennedy Ihianle (DoS; T&R; ECR)
Dr Kayode Owa – (Co-Supervisor; ECR)
Dr Pedro Machado – (Co-Supervisor; ECR)
Prof Eiman Kanjo – (Co-Supervisor; Advanced Career with many PhD completions as DoS and Co-Supervisors, head of the Pervasive Computing Group)

Academic Associate in Computer Science (Part Time Lecturer and Part Time PhD Study)

Job reference: 011431
Location: Clifton Campus
Closing date: 04/11/2022
Salary: Grade G (£29,762 – £34,475 p.a. pro rata)
Employment type: Fixed-term contract
Team: Computer Science
School / Directorate: School of Science & Technology

Job Description

Part-time academic post coupled with a part-time PhD scholarship fixed term for 5 years.

​​​​​​​NTU is a great place to work. We take care of our colleagues with flexible and competitive packages, and the opportunities for genuine professional progress. We respect and value all our staff because we know that it takes a strong and diverse workforce to maintain the kind of success we’ve achieved. Our award-winning research is celebrated around the world, and we’re proud of our strategic partnerships. It’s all down to the ability of our people to shape, create and innovate, in whatever capacity they work with us. Our continued success — underpinned by the number of prestigious national awards we’ve won — has hinged on two commitments: creating excellent global collaborations and harnessing the talents of all our people. NTU is a university at the peak of its powers, but we know we can still go even further. Join us in our mission to become ‘the university of the future’ — seize the challenge and adventure of working with an organisation where progress never stops. This is your career, reimagined.

We are specifically looking for an Academic Associate in Computer Science to support teaching (0.5 FTE). You will also have time beyond this to submit for a PhD (tuition fees paid).

Are you an academic looking for an innovative and successful university to take your next step? We are seeking to appoint an enthusiastic and talented Academic Associate in Computer Science. The Academic Associates are expected to contribute to the academic activities of the department (0.5 FTE) and concurrently undertake a part-time doctoral training programme leading towards the award of PhD. The successful post holder will be awarded a contract, which intrinsically links their teaching and learning commitments with their individual research activities. The Academic Associates will be working and completing a PhD in one of the research areas including 1) Computational Intelligence 2) Interactive Systems 3) Pervasive Computing 4) Cognitive Computing 5) Cybersecurity.

The Department of Computer Science resides within the School of Science and Technology on the Clifton Campus, and there are excellent collaborative opportunities with colleagues in other departments and research centres. Currently, the department comprises 60 academic staff and ~20 PhD research students, as well as large undergraduate and taught postgraduate cohorts. The department provides researchers with an exciting environment for undertaking high-quality, interdisciplinary work. ​​​​​​​

Alongside the online application, please complete a) a covering letter b) your CV c) a short statement (no more than one side of A4), outlining the topic you wish to research (or a research proposal). You may also identify potential supervisors for your research project. The shortlisted candidates will be required to deliver a 5-minute presentation about their proposed PhD research project.

If you have any specific queries in relation to this position that the online information does not cover, then please contact: Prof. Ahmad Lotfi, Head of Department of Computer Science, via CMPAdmin@ntu.ac.uk

NTU prides itself on being an inclusive employer. We value and celebrate equality in opportunities, and we welcome applications from people who reflect the diversity of our communities.

​​​​​​​This role is open to non-UK/Irish applicants subject to current UK Visas and Immigration (UKVI) rules. Please ensure that you have the appropriate right to work in the UK for this role and consult the Home Office website for further information.

We’re proud of how far we’ve come. With a shared vision, we are a community of more than 4,000 colleagues, all committed to our goal of becoming ‘the university of the future’. Do you have the passion to help us to go even further? www.ntu.ac.uk

Please note that this role is covered by the Rehabilitation of Offenders Act (1974) and successful applicants will be asked to declare any unspent criminal convictions.

​​​​​​​This role does not meet the minimum requirements set by UK Visas & Immigration to enable sponsorship of migrant workers. Therefore, we cannot progress applications from candidates who require sponsorship. As such, it is not possible for NTU to sponsor this role through either the Skilled Worker or Student sponsorship routes.

The post will cover the UK Postgraduate PhD research tuition fees (not International tuition fees) for the duration of the study (maximum 5 years). More details about research degrees at NTU is available from NTU Doctoral School.