University Doctoral Landscape Award (UDLA)
Advancing innovations in response to climate change and health inequalities
Project title: University Doctoral Landscape Award (UDLA)
Funded by:
Project duration: 2025–2031
°µÍø½âÃÜ staff:
Professor Lars Johanning
The University Doctoral Landscape Award aims to nurture a new generation of researchers in offshore renewable energy and health and medical technologies. Supported by a grant exceeding £1.7 million from the Engineering and Physical Sciences Research Council (EPSRC), the initiative will build upon established areas of research excellence led by the University’s
Centre for Decarbonisation and Offshore Renewable Energy and
Centre for Health Technology , tackling climate change challenges and health inequalities.
The UDLA programme aims to create a vortex of expanding research activities through its studentships, fostering the development of transformative technologies that benefit the economy, environment, and society.
By focusing on interdisciplinary themes, the UDLA will educate world-class researchers in an inclusive, connected, and resilient environment, encouraging deep and lasting academic engagement.
By collaborating with industry and non-academic organisations, the UDLA will co-produce net-positive solutions essential for a healthy and stable economy, in turn providing solutions for a sustainable, equal, and just society.
The UDLA programme will cultivate skills that underpin and promote multidisciplinary research and yield economic growth while attracting and retaining the next generation of global research leaders.
Research themes
Our research will be focused on priorities, supporting the thematic areas of decarbonisation and offshore renewable energy (ORE) and health and medical technologies (HMT), such as:
- Engineering net zero
- AI, digitalisation and data: driving value and security
- Transforming health and healthcare
Undergraduate summer internships 2026
As part of UKRI-EPSRC response to attract more home students to consider doctoral education, the °µÍø½âÃÜ has obtained funding allocation for undergraduate internships.
The purpose is to provide research experience for students undertaking an undergraduate degree within EPSRC's remit.
Internships will start in July, last 8 weeks, and be based in high-performing research teams. The minimum payment rate will be based on a minimum wage of £13.45.
Internship projects
Identifying Patient Clusters from Partially Observed Medical Data, an Unsupervised Learning Approach based on Dirichlet Processes
The project aims to develop novel clustering tools for health data based on Dirichlet Processes.
Objectives:
- To gain an understanding of and to produce a report about statistical models for partially observed medical data and about Dirichlet Process clustering methodology.
- To produce and include in an R package a clustering tool based on Dirichlet Processes and standard statistical methodology for right-censored data.
- To produce and include in an R package a clustering tool based on Dirichlet Processes and recently developed methodology for double interval censored length-biased data.
- To develop a grant-informing strategy to extend the tool produced by O3 to more sophisticated infectious disease scenarios, and to different statistical models.
Computational Modelling of Offshore Wind Turbine Tower Response under Coupled Wind–Wave Loading
The project aims to develop and apply a computational framework to analyse the response of offshore wind turbine towers under realistic environmental loading conditions.
Objectives:
1. To develop a validated CFD-based numerical model of an offshore wind turbine tower using OpenFOAM.
2. To simulate and analyse aerodynamic loading under wind-only conditions.
3. To investigate the coupled effects of wind and wave loading on tower response using advanced multiphase modelling (e.g., wave2Foam).
4. To evaluate fluid–structure interaction (FSI) effects and identify key factors influencing tower performance and stability.
5. To generate insights that support improved design methodologies for offshore wind systems and contribute to net-zero energy targets.
AI-Driven LoRaWAN Sensor Network Optimisation for Climate-Disease Forecasting in East Africa
The project aims to quantify the relationship between LoRaWAN IoT sensor network spatial density and climate-driven malaria forecast skill over East Africa, using geostatistical spatial field reconstruction as the methodological bridge between sensor network design and epidemiological modelling.
Objectives
1. Download and prepare fine-resolution (0.1°,~11 km) ERA5-Land temperature and precipitation data for the East Africa domain as the high-resolution observational ground truth, alongside the existing 0.5° AgERA5 datasets provided by Prof Morse's research group.
2. Compute empirical variograms from the fine-resolution climate datasets to characterise the spatial autocorrelation structure of temperature and precipitation across East Africa, quantifying the sub-grid variance currently invisible to the 0.5° malaria model grid.
3. Simulate three realistic LoRaWAN sensor network configurations — sparse, medium, and dense- derived from published LoRaWAN coverage and energy models, extracting synthetic sensor observations from the ground truth dataset at each density and adding calibrated measurement noise.
4. Apply Ordinary Kriging interpolation to reconstruct the full spatial climate field from each set of sparse sensor observations, using the fitted variogram as the spatial propagation model, and quantify reconstruction accuracy against the fine-resolution ground truth using MAE, RMSE, and spatial correlation metrics.
5. Integrate the reconstructed climate fields into the two key temperature-driven cycles of the Liverpool Malaria Model to generate malaria transmission risk outputs, comparing forecast skill across the three network configurations and against the coarse 0.5° baseline.
6. Produce a fully documented, reproducible Jupyter notebook implementing the complete pipeline: from climate data ingestion through the sensor network simulation, Kriging reconstruction, and malaria model integration, suitable for sharing with the broader research community.
7. Contribute to a joint peer-reviewed publication with Prof Morse (University of Liverpool) reporting the findings.
Persistent Homology for Directional Data with Applications to Diffusion MRI
The aim of this project is to develop and explore mathematical tools for analysing data with inherent directional structure, using persistent homology of circle-valued functions, and to assess their usefulness in diffusion MRI.
The objectives are to:
1. Introduce the student to modern research in topological data analysis and its mathematical foundations.
2. Implement recent theoretical advances in persistent homology for circle-valued functions.
3. Apply these methods to diffusion MRI data to explore how topological features reflect tissue organisation.
4. Provide the student with an experience of mathematically driven research applied to real medical imaging data, supporting progression to further study.
The project will combine mathematical study, computational work, and application to medical imaging data. The student will begin by studying the basic theory of persistent homology and its recent extension to circle-valued functions, developed by Broomhead and Pirashvili. Unlike classical scalar-valued approaches, this framework is suited to data with intrinsic directional structure.
The student will then assist in developing and testing prototype code that implements these methods, using small synthetic examples to understand how theoretical features manifest in practice.
In the final stage, the student will work with diffusion MRI data. Diffusion MRI produces local direction fields describing water diffusion in tissue, which naturally give rise to circle-valued functions. The student will construct such functions on small brain regions and analyse their persistent homology to explore whether the resulting topological summaries capture meaningful aspects of tissue structure.
Deliverables will include documented code, exploratory analyses, and a short report.
Real-time Motion Synthesis: Integrating High-fidelity Motion Capture with Robotic Arms for Virtual Production and Film-making Applications
This project aims to build a functional human-in-the-loop tracking system that allows a filmmaker to control an industrial robot instinctively. By capturing physical movement with a motion-capture camera system, the project will transform a high-tech laboratory into a 'digital atelier', enabling the robot to serve as a direct extension of the performer for audience-facing and virtual production applications.
This project is directly aligned with one of the main topics of the EPSRC UDLA research internship call, 'AI, Digitalisation, and Data', as it transforms human physical movement by AI digitalisation into a structured, high-value digital asset that a machine/robot can interpret and execute. It drives innovation in creative and filmmaking industries; it can also have wider applications in healthcare sectors, such as remote surgery.
Project objectives:
1. Seamless Motion Translation (The "Mirror" Effect)
- To create a "live-link" where the robot mimics human arm movements with no perceptible lag.
- Develop a software bridge that streams coordinates from the motion capture area directly to the robot's end-effector, ensuring that when the artist 'moves' in the air, the robot mirrors the stroke on a canvas in real-time.
2. Gesture-to-Stroke Precision
- To capture and replicate the subtlety of artistic and performance movements.
- Use the high-fidelity camera data to detect changes in velocity and acceleration. The objective is to ensure the robot doesn't just move from point A to B, but replicates the speed of a flicked wrist or the slowness of a careful detail.
3. Adaptive Workspace Mapping
- To allow a performer to perform large body-scale movements.
- Create a "scaling" feature where a large body movement by the human can be scaled down for intricate miniature work – for example, for scale virtual production, animation, character development, game design, or stop-motion and rostrum camera applications.
4. Safety-First Collaborative Environment
- To ensure the performer and robot can work in the same room safely.
- Program 'Virtual Boundary Walls' If the artist moves their arm too far or toward a prohibited area (like the camera stands), the system will automatically constrain the robot's movement to prevent physical collisions or equipment damage.
5. Performance Validation via "Physical Output"
- To prove the system works by producing an audience-facing short performance.
Pilot Study for Health and Care Innovative Solutions
The project aims to conduct a pilot study for the health and care innovative solutions that have been developed by SeCAM students under the supervision of the research team:
1. Virtual and physical dental robot friend: We have created a robot friend to help children understand dental health, what they expect when going to the dentist and encourage them to brush their teeth properly every day. The robot friend is presented in two forms: a virtual friend, which is a humanoid robot called QTrobot. Users can interact with the virtual robot via the mobile application at home. When they visit the dentist, they will check in with the physical robot, which can recognise them based on the shared information by the virtual robot.
2. IoT-based geofencing system: We have developed an IoT device which can track a user's location and share it with their carer. The users and carer can use the developed web application to define a geofence, an area which is considered as safe zone for the user. When the user goes out of the predefined geofence, the system will send a notification to the carer who will then be able to support them.
3. MindSHED: we have developed a mobile application called MindSHED which enable the students in the Dental Therapy programme at the Dental School to track their wellbeing. The application MindSHED aims to raise awareness and help students manage their well-being.
This pilot study aims to deploy and evaluate the implementation of the above solutions, using users’ acceptance measure and qualitative insights from users to inform a potential larger-scale study.
The objectives include:
1. Pilot test the feasibility and acceptability of the solutions
2. Explore participants’ experiences, perceived benefits, and barriers, including privacy concerns they may have when using the implemented solutions
Student eligibility
Consistent with the restriction on the doctoral landscape awards, students must be eligible for home fee status at the end of their undergraduate degree.
This is part of EPSRC’s work to improve the attractiveness of doctoral study to home students.
Students must be on an undergraduate degree related to EPSRC’s remit during the internship, either between their second and third years or between their third and fourth years.
The internship cannot be after their final year and cannot be part of a normal degree course.
How to apply
Interested candidates should contact secamexecsupport@plymouth.ac.uk by 17 June 2026 at the latest, providing a short CV and cover letter, and indicating the project of interest.
Explore our postgraduate research opportunities
. PhD/EngD studentship applications are now closed
Previous studentships
EngD studentships
Flexible bulge wave energy converter design integration AI-driven control system for hydrogen-electric maritime energy infrastructure The Development and Optimisation of Micro/Nano Plastic Sensors for Sustainable Green Hydrogen Production
PhD studentships
A surrogate model for the analysis of blood flow through compliant, size-mismatched arterial anastomoses in reconstructive surgery Metamaterials-based microwave devices for simultaneous wireless information and power transfer Generative AI for image and video reconstructions from brain signals Quantum computing in strong electromagnetic fields Multi-Objective Generative Model × Reinforcement Learning based autopilot for cleaner maritime navigation Human-AI Collaborative Framework for Deepfake Detection: Integrating Perceptual-Cognitive Mechanisms with Deep Learning Models Resource optimal link scheduling scheme for data delivery in marine applications using Space-Air-Ground-Sea Integrated Networks SAGSIN Federated Learning-Driven Decentralized AI Systems for Secure and Scalable Operations in Vehicular Networks Exploration of incremental knowledge addition for human activity analysis in open-world scenarios for public safety and surveillance Enhancing AI Performance with AMD's Neural Processing Units Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) Origami-Inspired Wave Energy Converter Through Dielectric Fluid Generator Improving floating offshore wind platform survivability using artificial intelligence to inform active control strategies Drone-Assisted Vision for Advanced Offshore Wind Turbine Maintenance Safety from wandering with enhanced privacy for people with mild dementia Developing a data science pipeline to extract high resolution models of eyes from MRI scans - Integrating multi-modal radiomics and AI co-pilot software to accelerate cancer detection and improve treatment decision-making
- Protecting privacy while preventing wandering in people living with mild dementia
- Enhancing AI medical applications on AMD cutting-edge AI hardware
- Physics informed neural surrogates for real time digital twins and CFD visualisation of offshore wind turbines
- Developing an origami-inspired wave energy converter through Dielectric Fluid Generator
- The performance of pontoons for offshore solar energy
- Advanced wireless power transfer technologies for offshore renewable energy systems
- Topological data analysis for multimodal neuroimaging: MRI and NIRS biomarkers of brain change
- Next-generation biodegradable microneedle electrodes for continuous heart monitoring
- Novel graphene field-effect transistors for RF to THz electronics and biosensing applications
- Development of pancreatic tumour treating field system
- Decarbonisation of coastal defences through adoption of vegetation-enhanced coastal infrastructure
UDLA studentships with a focus on decarbonisation and offshore renewable energy will be supported by the Centre for Decarbonisation and Offshore Renewable Energy and have access to our outstanding facilities such as the
Coastal, Ocean and Sediment Transport (COAST) laboratory and a wide range of expertise available across the University.
UDLA studentships with a focus on health and medical technologies will be supported through the Centre for Health Technology, and extensive experience from our MSCA Innovative Training Networks supporting early career researchers to research blood-based diagnostics of early-Alzheimer’s disease and brain tumours. Interdisciplinary research in the field of biomedical engineering and medical devices is led by the
Biomedical Research Group and the
MAterials and STructures (MAST) Research Group as well as the
Brain Research & Imaging Centre (BRIC) .
Centre for Decarbonisation and Offshore Renewable Energy
In response to climate change imperatives, we are bringing together a critical mass of leading research and expertise from across the °µÍø½âÃÜ. Through co-creation and collaboration with partners from business, government and key communities from across the globe, the Centre aims to be a beacon for the University’s whole-system transdisciplinary approach to solutions-oriented research, accelerating sustainable developments in decarbonisation and renewable energy.
Centre for Health Technology
The Centre for Health Technology unites digital health and technology expertise from across the University to drive the development, evaluation, and implementation of innovative technologies, products, services, and approaches aimed at transforming health and social care and creating a sustainable economy wellbeing.
Focusing principally on digitally-enabled innovations, our researchers work with a network of cross-sector partners, including NHS, industry, health and social care organisations and patient groups, to deliver research and development of international importance, enabled by the unique population and geographical characteristics of the South West region of England.
