Project Description

Functionally graded materials (FGMs) are a special branch of materials with smooth transitions in their microstructures and properties throughout the bulk. Compared to common composite materials with sharp interfaces (e.g. welding), FGMs exhibit evident advantages in their toughness and fatigue resistance. Recent developments in additive manufacturing (AM) technology also allow tailoring metallic FGM components by controlling the local printing parameters, which will significantly improve their performance in multi-function applications.

Despite the high demand in key industries, e.g. nuclear energy and aerospace, the design of metallic AM-FGM components targeting specific applications has not been attempted yet. The School of Engineering at Lancaster University is launching a PhD research project in collaboration with Southampton University, aiming to gain a predictive understanding of the Processing-Structure-Property-Performance (PSPP) relationships in metallic AM materials and develop a modelling protocol to assist the design of FGM components. This PhD research will combine physics-based mechanical models and data-driven methods, employing multiple simulation techniques to bridge AM processing with mechanical performance. Thereafter, the student will be expected to create a comprehensive database of the PSPP relationships of the target materials and provide valuable feedback to the FGM material design and AM process optimisation.

Qualifications and experience:

• The minimum academic requirement for admission is an upper second class UK honours degree at the level of MSci, MEng, MPhys, MChem etc, or a lower second with a good Master's, (or overseas equivalents) in a relevant subject.
• A good basis in computer programming is essential for the post.
• Knowledge in thermokinetics, physical metallurgy and dislocation theory is preferred.
• Excellent oral and written communication skills with ability to prepare presentations, reports and journal papers to the highest levels of quality.
• Excellent interpersonal skill to work effectively in a multi-disciplinary project area of research.

Funding Notes

This project is funded by Lancaster University. The funding covers the cost of the tuition fee for UK students and a standard tax-free RCUK stipend (£20,780 per annum for the 25/26 academic year) for 3.5 years for UK applicants. Non-UK candidates are welcome to apply but they would be required to fund the difference in fees. The successful candidate could start in October 2025 or January 2026.

Informal enquiries and how to apply

For informal enquiries, please contact Dr Wei Wen w.wen2@lancaster.ac.uk. Candidates interested in applying should send a copy of their CV together with a personal statement/covering letter addressing their background and suitability for this project to Dr Wei Wen.

Fully Funded PhD Studentship in Robotics and Control

Data-Driven Hybrid Motion–Force Control for Robust Human–Manipulator Interaction
Lancaster University – in collaboration with United Kingdom National Nuclear Laboratory (UKNNL)

We invite applications for a fully funded PhD studentship at Lancaster University’s School of Engineering, in partnership with United Kingdom National Nuclear Laboratory (UKNNL). This exciting project will develop novel data-driven, robust, and adaptive control methods for human–robot interaction and teleoperation, with direct applications in nuclear robotics, hazardous environment manipulation, and beyond.

Project Overview

Teleoperation is a critical enabler for safe and efficient operation in hazardous environments such as nuclear decommissioning. However, current industrial solutions suffer from limitations under uncertainty, time delays, and noisy sensing.

This PhD project will design and experimentally validate a hybrid motion–force control framework that ensures precise end-effector positioning while maintaining robust and adaptive force regulation under real-world conditions. Research will include:

• Development of nonlinear robust adaptive controllers and disturbance observers.
• Design of bilateral teleoperation schemes that enhance transparency and stability under communication delays.
• Integration of data-driven approaches for force estimation and safety.
• Experimental validation on industrial robotic platforms at the UKNNL Hot Robotics Facility.

The project provides the opportunity to work on cutting-edge robotics challenges with significant industrial impact, supported by state-of-the-art facilities at both Lancaster University and UKNNL.

Supervisory Team

• Dr Allahyar Montazeri (Lead Supervisor, School of Engineering, Lancaster University; Data Science Institute Member)
• Prof Plamen Angelov (Co-Supervisor, School of Computing and Communications, Lancaster University; Data Science Institute Member)

Training and Development

The successful candidate will receive a tailored training programme including:

• Hands-on training with ROS2, MATLAB/Simulink, and CoppeliaSim.
• Access to world-class robotics laboratories and facilities.
• Opportunities to engage with national and international conferences, workshops, and training events.
• Insight into the nuclear sector through industrial collaboration with UKNNL.

Funding

• Duration: 4 years (3.5 years EPSRC Doctoral Landscape Award + 0.5 years UKNNL extension)
• Coverage: UKRI minimum stipend, tuition fees for Home students, and a research training support grant.
• Additional support for consumables, maintenance, and travel.

Eligibility

• Open to UK Home students only, due to clearance requirements for UKNNL facilities.
• Applicants should have (or expect to obtain) a First or Upper Second-Class degree (or equivalent) in Engineering, Control, Robotics, Computer Science, or a related discipline.
• Strong mathematical and programming skills (MATLAB, Python, or C++) are highly desirable.

Application Process

Applicants should submit:

1. A full CV.
2. A one-page cover letter outlining their motivation and suitability for the project.
3. Reference letter from two academics commenting on the candidate abilities.

Applications will be considered on a rolling basis until the position is filled, with an expected start date of January 2025.

For informal enquiries, please contact Dr Allahyar Montazeri (a.montazeri@lancaster.ac.uk).

About the Project

Applications are invited for a funded (UK fees and stipend) PhD studentship in the Department of Engineering at Lancaster University under the supervision of Professor Malcolm Joyce concerning accelerator mass spectrometry studies of trace radioactivity in industrial effluents. This research is funded by the National Nuclear Laboratory and is suitable for UK nationals graduating in Physics, Chemistry, or related STEM subject. Candidates with related mass spectroscopy laboratory experience by way of, for example, an undergraduate project or prior work experience are encouraged to apply.

Funding

Funded by the National Nuclear Laboratory, this studentship is available for UK applicants and fully funded for 3.5 years, covering fees and a maintenance grant (i.e. £20,780 for 25/26 academic year) (all tax free). The successful candidate could start in January 2026.

Informal enquiries and how to apply

For informal enquiries, please contact Prof Malcolm Joyce (m.joyce@lancaster.ac.uk). Candidates interested in applying should send a copy of their CV together with a personal statement/covering letter addressing their background and suitability for this project to Prof Malcolm Joyce by the closing date: 30 November 2025.

Supervisors

1) Dr. M. Sergio Campobasso: m.s.campobasso@lancaster.ac.uk

2) Prof. Adrian Jackson: a.jackson@epcc.ed.ac.uk

3) Dr. Evgenij Belikov: E.Belikov@epcc.ed.ac.uk

4) Dr. Wenxin Zhang: Wenxin.Zhang@glasgow.ac.uk

Project Description

OVERVIEW AND BACKGROUND. The extraction of energy from the wind yields the formation of low-speed regions (wakes) behind wind farms (WFs). Wakes are particularly persistent offshore [2], and were recently shown to affect the heat exchange between sea and atmosphere, due to reduced convective heat transfer close to the sea surface [1]. With worldwide offshore wind capacity en-route to achieve 2,000+ GW already by 2050, WF wakes may alter ocean dynamics and marine ecosystems to extents comparable to anthropogenic climate change [2]. Evaluating wakes’ environmental impact credibly requires regional- to mesoscale climate simulations with high-fidelity WF parametrizations at temporal and spatial resolution beyond present supercomputers’ capabilities. Using Graphics Processing Unit (GPU) computing [3], this project will develop the code infrastructure to support these simulations on exascale machines, demonstrating prototype physical investigations using the developed technology.

METHODOLOGY. Two community codes for short-to-long term climate modelling are considered: the Weather Research and Forecasting (WRF) model [4], and the Model for Prediction Across Scales (MPAS) [5]. The codes feature similar models of atmospheric physics, but use different numerical methods. WRF uses structured grids with nested domains to increase resolution in WF wake regions, whereas MPAS uses a single unstructured Voronoi grid with controllable local refinement. WRF has state-of-the-art WF parametrisations [6,7] but little GPU work reported; MPAS uses GPU acceleration but has little work reported on WF parametrization.

This research aims at combining the strengths of both codes to develop a reliable exascale-scalable code for the considered problem. The choice of the baseline code for the project’s core development and demonstrations will follow the teaser projects (TPs) below, which offer hands-on training in climate modelling, wind farm aerodynamics and distributed-memory and GPU parallel computing, and assess the codes’ strengths. Following the TPs, the student will focus on specific topics, e.g. improving the overall code GPU framework or optimizing the parallelized WF model in existing GPU framework, depending on the code selected.

The TPs will share one test case, to compare the two codes’ predictive capabilities and computational performance (execution speed) without GPU acceleration. The GPU development work will be performed on Lancaster University’s HEC cluster and the Bede supercomputer [9].

Teaser project 1 (TP1): WRF-based. To investigate and optimize the predictive capabilities of the two WF parametrizations [6,7] in WRF, analyses (TC1) of the North Sea area containing two real WFs [10] will be performed. The capabilities of both models to predict wind turbine (WT) and WF wakes will be optimised using regression methods for the models’ parameters, and lidar and satellite wind speed measurements to steer the optimization. Measured WT power will also be used in the process, as this parameter is affected by wakes. A second test-case (TC2) without WFs will be used to perform parallel profiling studies of WRF, identifying the code’s computationally most intensive parts and familiarising with its structure. These analyses will identify the code sections that would benefit most from GPU acceleration. TC2 will also be used to cross-compare the predictive capability of WRF and MPAS, assessing it by comparing predicted near-sea surface wind speed maps to measurements from satellites and lidars. Boundary and initial conditions for TC1 and TC2 will be taken from the ERA5 global climate reanalysis [8].

Teaser project 2 (TP2): MPAS-based. First, TC2 will be set up and analyzed without GPUs to cross-compare the computational speed and prediction capabilities of wind speed field of MPAS and WRF. Then, more comprehensive TC2-based parametric analyses of the performance of MPAS using different numbers of CPUs and GPUs will be undertaken to study the dependence of the computational performance of the hybrid parallelization on the CPU and GPU counts, and determine the largest achievable acceleration and the corresponding optimal ratio of GPU and CPU counts - an information paramount for exascale porting. These analyses also enable familiarising with the MPAS structure, knowledge needed to optimally merge wind farm models with the MPAS GPU infrastructure.

TP1 OBJECTIVES: A) Familiarise with WRF: assess predictive capabilities of 3D wind fields with/without WFs; analyze/optimize best suited WF parametrization: B) Assess computational performance and estimate potential of GPU acceleration.

TP2 OBJECTIVES: A) familiarise with MPAS: assess predictive capabilities of 3D wind fields; investigate performance of hybrid CPU/GPU parallelisation; B) Investigate optimal integration of WF model into GPU framework.

References

1) Akhtar, N. et al., Impacts of accelerating deployment of offshore wind farms on near-surface climate. Sci Rep 12, 18307 (2022). https://doi.org/10.1038/s41598-022-22868-9.

2) Platis A. et al., First in situ evidence of wakes in the far field behind offshore wind farms. Sci Rep. 2018;8(1):2163. https://www.nature.com/articles/s41598-018-20389-y

3) Hijma, P., et al. Optimization techniques for GPU programming. ACM Computing Surveys 55.11 (2023): 1-81, https://dl.acm.org/doi/10.1145/3570638.

4) Powers, J.G., et al. The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society 98.8 (2017): 1717-1737. (see also: Weather Research and Forecasting (WRF) model, https://www.mmm.ucar.edu/models/wrf).

5) Skamarock, W.C., et al. A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tesselations and C-grid staggering. Monthly Weather Review 140.9 (2012): 3090-3105. (see also: Model for prediction across scales (MPAS), https://www.mmm.ucar.edu/models/mpas).

6) Fitch, A. C. et al., Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWRD-11-00352.1, 2012.

7) Volker, P. et al., The Explicit Wake Parametrisation V1.0: A Wind Farm Parametrisation in the Mesoscale Model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015.

8) ERA5 Global Climate Reanalysis. https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5.

9) N8 CIR, The Bede supercomputer. https://n8cir.org.uk/bede/.

10) Orsted, Offshore wind measurement and operation data. https://orsted.com/en/what-we-do/renewable-energy-solutions/offshore-wind/offshore-wind-data.

Funding

The Scholarship is part of the ExaGEO Doctoral Training Programme funded by the National Environment Research Council. Further information is available at https://www.exageo.org/.

Informal enquiries and how to apply

Informal enquiries to Dr. M. Sergio Campobasso: m.s.campobasso@lancaster.ac.uk. Applications should be made to: https://www.exageo.org/apply/ by Friday 9th January 2026.

Supervisors:

1. Professor Colin Boxall, Lancaster University
2. Dr Mick Bromley, Lancaster University
3. Dr Josh Turner, UKNNL Ltd
4. Jo Pugh, Sellafield Ltd

About the Project

Ruthenium is a fission product possessed of two relatively long-lived stable isotopes: Ru-103 (half life = 39.8 days) and Ru-106 (half life = 1 year). Both isotopes are present in UK spent fuel and so have had to be accounted for during the reprocessing or disposal of that fuel. At a number of stages during the processing of spent fuel, ruthenium can be exposed to high nitric acid, high temperature conditions that may lead to its transfer into the gas phase as ruthenium tetroxide. Two such stages are the dissolution of spent fuel into concentrated nitric acid at the start of reprocessing, and the vitrification of ruthenium into a glass waste form after reprocessing has occurred.

Volatilisation is to be avoided as the resultant gas phase ruthenium may then redeposit within metal pipework elsewhere in the plant which will then have to be decontaminated. However, ruthenium volatilisation occurs at unexpectedly low temperatures. Whilst RuO2 is not seen to volatilise below 900oC, gaseous ruthenium oxides have been seen to evolve from solutions of Ru in nitric acid at temperatures as low as 150oC – making the management of ruthenium difficult during reprocessing and vitrification.

Thus, given its volatile nature and high specific radioactivity ruthenium presents a strong challenge to the nuclear industry in effectively managing its abatement. Key challenges are to fully understand the highly complex solution/solid state chemistries that obtain not only under conditions relevant to dissolvers, evaporators and vitrification plants, but also in the decontamination methods used in its clean up. Using a combination of chemical, analytical and engineering approaches, we shall seek to address these challenges in this PhD.

This project is a collaboration between Lancaster University, The UK National Nuclear Laboratory (UKNNL) and Sellafield Ltd. Experimental work will be conducted primarily in Lancaster’s UTGARD (Uranium-Thorium beta-Gamma Active R&D) Lab.

This project is offered through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training).

To apply for this PhD, please first send an email expressing interest to SATURN CDT and Professor Colin Boxall. You should then apply using the online application form available on the Lancaster Postgraduate Study website.

You should address your background and suitability for this project in your personal statement.

Funding Notes

Supported by UKRI/EPSRC, Lancaster University, UKNNL and Sellafield Ltd through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2025. For UK applicants the studentship is fully funded for 4 years, covering fees and a maintenance grant (£20,780) (all tax free).

About the Project

This four-year PhD project aims to support the UK Atomic Energy Authority’s (UKAEA) mission to advance sustainable fusion energy and maximize its scientific and economic benefits. Future fusion reactors will harness the reaction between deuterium and tritium to produce significant amounts of low-carbon energy. Since tritium is a scarce resource, it must be generated in-situ through the transmutation of lithium, necessitating the design of a sophisticated breeder blanket that surrounds the plasma chamber. The breeder blanket operates under extreme environmental conditions, including high temperatures and intense neutron irradiation. These conditions span multiple time and length scales and involve various interdependent physical phenomena, making accurate prediction and design challenging. Consequently, developing breeder blankets requires multiphysics models that account for neutron and thermal transport, stress and strain, and fluid mechanics. UKAEA’s current multiphysics models include a basic framework for tritium transport through materials. However, this existing model lacks the capability to account for complex phenomena such as radiation-driven diffusion. Therefore, the primary goal of this project is to develop advanced tritium diffusion models for fusion reactors and integrate them into UKAEA’s multiphysics modelling framework. These models will be further parameterized using atomistic simulation techniques, including classical Molecular Dynamics (cMD) and Density Functional Theory (DFT). Once fully implemented and parameterised the multiphysics model will be employed to develop advanced breeder blankets for reactors, such as the UK Government’s Spherical Tokamak for Energy Production (STEP).

This project is offered through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training).

For informal enquiries, please contact Dr Samuel Murphy.

Candidates interested in applying should first send an email expressing interest to SATURN CDT as soon as possible and by the closing date: 31 May 2025.

Funding Notes

Supported by the UK Atomic Energy Authority (UKAEA), UKRI/EPSRC and Lancaster University through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2025. For UK applicants the studentship is fully funded for 4 years, covering fees and a maintenance grant (£19,237) (all tax free).

Supervisors:

1. Dr David Cheneler - Lancaster University
2. Dr Stephen Monk - Lancaster University
3. Dr James Graham - Sellafield Ltd

About the Project

Proposed here is the development of an automated detection and fingerprinting method using machine learning and spectra unfolding techniques for the real-time, in-situ monitoring and determination of weak beta-emitting radionuclides found in groundwater around nuclear sites without the need for sampling. This will utilise state-of-the-art technology recently developed by the research team, capable of detecting beta-emitting radionuclides in water.

Remote monitoring of groundwater sites is notoriously difficult owing to the array of chemical and physical contaminants that can potentially be found due to the varied past uses of sites, such as Sellafield, including non-nuclear activities. Detection and identification of beta-emitting radionuclides such as 3H, 40K, 90Sr, 125Sb, etc. in boreholes is a complex but important task due to the emission of ionising radiation, which can be hazardous to human health. Hence the requirement to monitor groundwater to ensure it meets national and international legislation. This task is difficult as the beta particles emitted have broad, overlapping energy spectra, and are absorbed within a very short distance from creation in water. Currently, the assay of radionuclides producing weak beta radiation is undertaken via the sending of samples to 3rd party labs for extraction and analysis – a costly and time-consuming method.

This project will build on a previous successful NDA bursary funded project ‘In-situ Real-time Monitoring of Waterborne Low Energy Betas’ (NNL/UA/006) led by the research team here. In this project, a prototype system was developed [1, 2] capable of detecting weak beta-emitting radionuclides, such as tritium, in groundwater. The detectors developed were designed to fit in boreholes and detect radionuclides in water without having to remove samples from the borehole. An efficient data management system [1] was also developed to facilitate extended testing and communications required for deployment.

The proposed project will use this technology directly but extend the functionality to automatically detect and quantify the concentrations of individual weak beta-emitting radionuclides, such as tritium and 14C, in the presence of 90Sr, which is the dominant high-energy beta-emitting radionuclide in groundwater on site at Sellafield. The research will focus on developing machine learning algorithms to extend total beta analysis to individual weak beta-emitting radionuclides, automating the analysis, and negating the need for sampling and laboratory separation and chemical analysis. It will become far cheaper to screen boreholes frequently.

Interested candidates are strongly encouraged to contact the project supervisor Dr David Cheneler to discuss their interest in and suitability for the project prior to submitting an application, also register their interest with the SATURN CDT for this project.

Applicants should have a minimum of an upper second-class honours degree in electronic engineering, mechatronic engineering, computer science, or a related technical subject.

Funding Notes

Supported by UKRI/EPSRC, Lancaster University and the NDA through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2025. For UK applicants, the studentship is fully funded for 4 years, covering fees and a maintenance grant (£20,780) (all tax free).

References

[1] Compact Back-End Electronics with Temperature Compensation and Efficient Data Management for In Situ SiPM-Based Radiation Detection. https://doi.org/10.3390/s23084053
[2] Laminated Flow-Cell Detector with Granulated Scintillator for the Detection of Tritiated Water. https://doi.org/10.3390/radiation3040017

Supervisors:

1. Professor Colin Boxall, Lancaster University
2. Dr Thomas Carey, UKNNL Ltd
3. Dr Adam Lang, Environment Agency

About the Project

The UK government is updating its guidance to help responsible authorities prepare for an uncontrolled release of radioactivity to the urban environment, due to either a nuclear accident or a radiological terrorist attack. Previous radiation emergencies in the UK and overseas have shown that decontamination of buildings and infrastructure after either such event can produce large volumes of waste which can be difficult to manage.

Historical nuclear accidents indicate that the nature of caesium-137 (¹³⁷Cs) contamination on different grades of concrete can be markedly different following a radioactive fallout event in an urban environment. These inconsistencies can significantly influence the efficacy of a candidate decontamination technique and the resulting waste volume. Complexities in radionuclide contamination behaviour therefore make it challenging to develop robust decontamination and waste management strategies for radiation incidents.

This PhD project aims to fill this gap by investigating the physical and chemical processes which promote ¹³⁷Cs accumulation on urban concrete materials during a radiation incident. Decontamination and waste management implications of ¹³⁷Cs contamination phenomena will also be assessed.

This project will aim to:

1. Through use of non-radioactive Cs surrogates and ¹³⁷Cs (spiked) samples, explore the interactions between ¹³⁷Cs fallout and UK concrete construction materials using laboratory rigs and advanced characterisation techniques
2. Determine the effectiveness of decontamination methods for removing ¹³⁷Cs from urban concrete infrastructure and evaluate the waste volumes and activities generated

The outputs of this project will be used to inform UK radiation emergency planning. This includes improving models developed by the UK and/or international partners to estimate the waste consequences of recovering from urban contamination events.

This project is a collaboration between Lancaster University, The UK National Nuclear Laboratory (UKNNL) and the UK’s Environment Agency (EA). Experimental work will be conducted primarily in Lancaster’s UTGARD (Uranium-Thorium beta-Gamma Active R&D) Lab using non-radioactive Cs surrogates and ¹³⁷Cs (spiked) samples, with opportunities to conduct specific higher activity ¹³⁷Cs-based radiological tests within the facilities at the National Nuclear Laboratory.

This project is offered through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training):

To apply for this PhD, please first send an email expressing interest to SATURN CDT and Professor Colin Boxall. You should then apply using the online application form available on the Lancaster Postgraduate Study website.

You should address your background and suitability for this project in your personal statement.

SATURN_Nuclear_CDT

Supervisors:

1. Professor Colin Boxall
2. Dr Fabrice Andrieux
3. Matthew O’Sullivan, Sellafield Ltd
4. Tom Bainbridge, Sellafield Ltd

Since the 1950s, Sellafield site has been the hub of the UK nuclear industry. Across this time the site has been used for various activities including, experimental reactors, commercial power generation, fuel and waste storage and fuel reprocessing.

These activities have generated aluminium wastes that are planned to be retrieved from existing facilities and held in modern storage facilities before eventual conditioning for disposal within a Geological Disposal Facility (GDF). This includes British Experimental Pile fuel (BEP0) from the Windscale Pile reactors (stored within ponds) as well as miscellaneous aluminium items disposed of to the Magnox Swarf Storage Silo (MSSS) which received primarily Magnox swarf resulting from de-canning of Magnox fuel between the 1960s and the 1990s.

To appropriately manage these wastes, Sellafield must understand aluminium behaviour in different storage environments. Evidence for the corrosion of aluminium in the environments at Sellafield is limited and often contradictory. Experimental trials have previously concluded that aluminium will not corrode in the presence of Magnox corrosion product. Observations have been made from inspection of material in the ponds that some aluminium items have corroded whilst others have not. Whereas observations from retrieval activities so far in MSSS indicate that the aluminium waste items in this facility are likely to have largely corroded away.

The aim of this project is to study the corrosion of aluminium in environments analogous to current and future storage conditions at Sellafield. The results of this project will influence strategy for retrieval, storage conditioning and disposal of nuclear wastes.

This project is a collaboration between Lancaster University and Sellafield Ltd. Experimental work will be conducted primarily in Lancaster’s UTGARD (Uranium-Thorium beta-Gamma Active R&D) Lab.

This project is offered through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training).

To apply for this PhD, please first send an email expressing interest to SATURN CDT and Professor Colin Boxall. You should then apply using the online application form available on the Lancaster Postgraduate Study website.

Funding Notes

Supported by Sellafield Ltd, UKRI/EPSRC and Lancaster University through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2025. For UK applicants, the studentship is fully funded for 4 years, covering fees and a maintenance grant (£20,780) (all tax free).

Supervisors:

1. Dr Stephen Monk - Lancaster University
2. Dr David Cheneler - Lancaster University
3. Dr Jeremy Andrew - NRS Dounreay

About the Project

Due to space and dose constraints, human access is rarely achievable within pipework across the nuclear estate. However, pipe-crawling robots are a current hotbed of research activity with numerous devices developed for similar applications.

This project involves the development of a modular robot/sensor network capable of traversing 50 mm pipework featuring swept bends and T-pieces. This follows on from another PhD study concerned with the development of an inchworm locomotive (in collaboration with Dounreay). Within this project, the successful candidate will develop several interchangeable modules.

• A High-Resolution Imaging module using standard machine learning techniques at the front to provide vision data enabling navigation whilst also providing information to the user concerning contaminants such as residual liquor.
• Corrosion Sensing module utilising one of numerous options for corrosion sensing in the literature. We would anticipate using ultrasonic methods as they appear to be the most suitable within pipes of this nature. Other groups have also looked at using fibre optics to determine corrosion levels – so there are numerous options to try.
• A spray delivery module utilising an electrically actuated system which will be developed with a syringe mechanism. This module could utilise a decontaminant such as nitric acid or a rapid drying fixative depending on exact application.
• A bespoke radiological instrumentation module – involving bare photodiodes to monitor alpha particles, B-10 coated ones to detect neutrons and CeBr₃ detectors to detect gamma and beta via discrimination algorithms. All sensors will provide spectroscopic (energy) discrimination to enable specific nuclide identification. This is a significant challenge (low alpha in presence of high beta for example).
• A Ramen Spectroscopy module which will be used to better determine analytes within the pipework such as liquor.

It is intended that the device be connected via umbilical containing both optical fibre communications (limited in length by factors such as friction) and power leads.

This project would suit a candidate with interests in areas such as electronics and robotics, although any weaknesses will be rectified via training courses to optimise chances of project success. This project is offered through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training).

Interested candidates are strongly encouraged to contact the project supervisor Dr Stephen Monk to discuss their interest in and suitability for the project prior to submitting an application, also register their interest with the SATURN CDT for this project.

Applicants should have a minimum of an upper second-class honours degree in electronic engineering, mechatronic engineering, computer science, or a related technical subject.

Funding Notes

Supported by UKRI/EPSRC, Lancaster University, UKNNL and the NDA through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2025. For UK applicants the studentship is fully funded for 4 years, covering fees and a maintenance grant (£20,780) (all tax free). Please note: this opportunity is only for UK students due to funding and security reasons.

References

[1] Blewitt, Gabrielle, David Cheneler, Jeremy Andrew, and Stephen Monk. "A review of worm-like pipe inspection robots: research, trends and challenges." (2024).