We offer a range of PhDs funded by different sources, such as research councils, industries or charities. Here you will work with internationally respected academics, post-doctoral research associates and technicians.
To apply for a funded PhD, please read the advertised project information carefully as requirements will vary between funders. The project information will include information such as funding eligibility, application deadline dates and links to application forms. We will only consider applicants who have a relevant background and meet the funding criteria.
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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)
Professor 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:
A full CV.
A one-page cover letter outlining their motivation and suitability for the project.
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 April 2026.
Applications are invited for a fully funded PhD studentship (UK fees and stipend) in the School of Engineering at Lancaster University, supervised by Professor James Taylor and colleagues. Jointly funded by the EPSRC and an industrial partner, this project will explore the application of machine learning and digital twin technologies to the nuclear fuel cycle. The studentship is open to suitable graduates in Engineering, Physics, Mathematics, or a closely related STEM discipline.
Industry 4.0 technologies have the potential to transform nuclear energy production. This PhD focuses on applying advanced data-driven methods to an innovative, integrated dry-route uranium conversion process. Unlike conventional wet methods, this approach reduces environmental impact and production costs by eliminating liquid water from the process.
Although designed for autonomous operation, performance can be influenced by complex chemical reactions and changing environmental conditions that are not fully captured in real time. Quality assessment is currently largely offline, with manual intervention. Early studies using limited datasets have demonstrated that machine learning models can successfully predict uranium dioxide output quality from process signals such as pressures, flow rates, and temperatures.
This project will move substantially beyond existing approaches by exploiting richer datasets to develop a high-fidelity digital twin of the manufacturing process, with the PhD researcher developing novel data-driven modelling and analysis techniques. The goal is to enable real-time monitoring, adaptive control, predictive maintenance, and asset management in an industrial environment.
Your Learning Experience
This PhD offers a unique opportunity to combine cutting-edge research with real industrial impact.
You will develop hands-on expertise in:
Data science and machine learning for complex physical systems
Digital twin development and validation
Nuclear fuel cycle fundamentals
Predictive modelling and uncertainty quantification
Working within a safety-critical industrial setting
The project will begin with familiarisation of existing datasets and models, progressing rapidly to the acquisition, pre-processing, and exploratory modelling of newly collected plant data. You will develop predictive models linking process conditions to product quality, before constructing and validating a digital twin of the full manufacturing process.
Forecasting performance will be evaluated via the industrial partner’s facility, giving you direct exposure to real-world deployment and industrial collaboration. Your training will be supported by structured university provision in nuclear engineering fundamentals, advanced computational and statistical methods, project management, and scientific writing. On-site training, secondments, and close industry engagement will further strengthen your professional development.
In the later stages, you will explore how the developed methodologies can be transferred to other safety-critical processes across the nuclear fuel cycle.
This project offers the chance to work at the intersection of AI, advanced manufacturing, and nuclear energy, contributing to safer, cleaner, and more efficient fuel production while building highly sought-after technical and industrial expertise.
Funding and eligibility
This project will be funded by an IDLA studentship (formerly iCASE) for 4 years. This provides for a tax-free stipend at UKRI rates and university fees at the home (UK) rate. Non-UK students might be eligible for higher fee rates and should discuss this with Professor Taylor. The project also has funding to cover travel to the industry partner and conferences/workshops.
Informal enquiries and how to apply
For informal enquiries, please contact Professor James Taylor (c.taylor@lancaster.ac.uk) or other colleagues involved, including Professor Malcolm Joyce and Dr Xiandong Ma at Lancaster University, or Professor Paul Murray at Strathclyde University (https://www.lancaster.ac.uk/engineering/research/airs-nfm/). 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 Professor James Taylor.
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 (that is £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 Professor 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 Professor Malcolm Joyce.
About the Project
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 parametrisations 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 parametrisation.
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 optimising the parallelised 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 optimise the predictive capabilities of the two WF parametrisations [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 optimisation. 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 analysed 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 parallelisation 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; analyse/optimise best suited WF parametrisation: 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.
Funding Notes
The Scholarship is part of the ExaGEO Doctoral Training Programme funded by the National Environment Research Council. Further information is available at View Website.
This project is a unique opportunity to join a vibrant research team from Lancaster University, University of Manchester and The Cockcroft Institute at Daresbury Laboratory, Warrington, UK, developing world leading concepts for novel acceleration using laser-generated THz pulse.
In the drive toward the understanding and exploitation of laser generated THz pulses through structures that can mediate the interaction process for the control of electron beam properties, dielectric lined waveguides (DLWs) have emerged as one of the most promising solutions. These structures consist of a metallic waveguide lined internally with thin layers of dielectric and can be designed to enhance the interaction between a strong THz field and relativistic electron bunches. By choosing the appropriate electromagnetic mode, these can be optimised as THz-driven deflecting structures for ultrafast diagnostics and beam manipulation. However, several challenges in the design and optimisation of these structures exist, such as the integration of efficient coupling sections to convert the Gaussian cross section mode typically generated by the external THz source into the correct waveguide mode. This project seeks to progress from previous leading work at Cockcroft in rectangular section THz DLWs for beam manipulation. The aim is to develop novel structures for THz-driven beam deflection that maximise efficiency of external THz pulse coupling into the DLW deflecting mode. The project will include studies of the manufacturing and tuning of these structures, longitudinal and transverse beam dynamics, and experimental characterisation.
The 3.5-year project is expected to start in October 2026. The work is mainly based on numerical computation, using full wave electromagnetic codes such as CST Particle Studio. The work will include the development of prototype structures, their test in a THz bunker and data analysis. We welcome applications from students holding or expected a first or upper second-class degree in physics or electronic engineering or other appropriate qualification. Candidates should have a good understanding of electromagnetic theory. Computational skills are desirable but not essential.
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 Letizia and additionally follow the application process detailed .
Funding and eligibility: Upon acceptance of a student, this project will be funded by the Science and Technology Facilities Council for 3.5 years. This consists of a tax free stipend at UKRI rates, university fees at the home (UK) rate, plus support for travel to conferences and workshops. A full package of training and support will be provided by the Cockcroft Institute, and the student will take part in a vibrant accelerator research and education community of over 150 people. Non-UK students will be eligible for higher fee rates and should discuss this with the project supervisor.
How to apply: Apply at the Cockcroft Institute PhD webpage. For full consideration for funded awards, please apply by Jan 31st 2026.
Anticipated Start Date: October 2026 for 3.5 Years
AI-Driven Design of Microfiber Removal Systems
Supervisors: Dr. Luigi Capozzi (Chemical Engineering) and Dr. John Hardy (Chemistry)
Project description: Microfibers from textiles are a major source of microplastic pollution, posing significant environmental and health risks. Current wastewater treatment methods struggle to effectively capture these particles, often relying on energy-intensive filtration systems. This PhD project explores a novel approach to microfiber removal, using CFD-DEM simulations, AI, and experimental validation to design and optimise systems that improve capture efficiency while reducing energy consumption.
The research will be conducted in collaboration with Dr. Mariacristina Cocca at the Institute of Polymers, Composites and Biomaterials (IPCB-CNR, Naples). By integrating advanced simulation techniques with material design, the project offers a scalable and sustainable solution for wastewater treatment. This interdisciplinary approach provides a unique opportunity to work at the intersection of computational engineering and polymer science. Join us in developing next-generation technologies to safeguard our aquatic ecosystems.
General eligibility criteria: Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.
Project specific criteria: The ideal candidate will have an interest in fluid mechanics, computational modelling, chemistry, material science and sustainable engineering solutions. Experience with numerical simulations or materials processing is beneficial but not required.
Studentship funding: A tax-free stipend will be paid at the standard UKRI rate; £20,780 in 2025/26. This is a fully funded studentship of 3.5 years for UK/Home students.
Enquiries: Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Dr. Luigi Capozzi (l.capozzi@lancaster.ac.uk) for more information.
Dates
Deadline for candidate applications: 23rd March 2025
You will receive a generic acknowledgement in receipt of successfully sending the application documents.
Please note that only applications submitted as per these instructions will be considered.
Please note that, if English is not your first language, you will be required to provide evidence of your proficiency in English. This evidence is only required if you are offered a funded PhD and is not required as part of this application process.
Outmuscling snakebite venoms with protein shakes: saving snakebite victims from wounds and limb loss in the Global South with bodybuilding food supplements
Supervisors: Dr. Timothy Douglas, School of Engineering and Dr. Steven Hall, Department of Biological and Life Sciences
Project description: Snakebites can lead to tissue necrosis and chronic wounds, which in turn leads to limb loss, drastically reduced quality of life, or death. The human and economic burden is great, particularly in the Global South.
There is a pressing need to treat snakebites rapidly in-the-field, delivering drugs to neutralize snake venoms, particularly as victims cannot receive immediate medical treatment.
In this interdisciplinary project linking toxicology, pharmacology and engineering, you will:
Deliver novel cocktails of drugs to neutralize snake venoms to prevent or treat tissue necrosis and wound formation.
Develop hydrogels capable of delivering these drug cocktails which can be applied as a dressing to snakebite wounds immediately. These dressings will be based on bodybuilders’ protein supplements, a low-cost material available in vast quantities.
Test the efficacy of your drug cocktail-loaded hydrogel dressing in wound models.
General eligibility criteria: Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.
Project specific criteria: The ideal candidate will have an interest in conducting experimental, interdisciplinary work and collaboration with colleagues from different countries. We welcome candidates from any scientific background, be it medical, biological, chemical or engineering!
Studentship funding: A tax-free stipend will be paid at the standard UKRI rate; £20,780 in 2025/26. This is a fully funded studentship of 3.5 years for UK/Home students.
Enquiries: Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Dr. Timothy Douglas (t.douglas@lancaster.ac.uk) and Dr. Steven Hall (s.r.hall@lancaster.ac.uk) for more information.
You will receive a generic acknowledgement in receipt of successfully sending the application documents.
Please note that only applications submitted as per these instructions will be considered.
Please note that, if English is not your first language, you will be required to provide evidence of your proficiency in English. This evidence is only required if you are offered a funded PhD and is not required as part of this application process.
Interdisciplinary Safe Shared Autonomy for Ageing Care with Humanoid Robots (G1-CARE)
Supervisors: Dr Ziwei Wang (Engineering); Dr Elmira Yadollahi (SCC); Professor James Taylor (Engineering); Professor Plamen Angelov (SCC)
Project description: This PhD will develop and validate a safety-assured shared-autonomy framework for a superstar humanoid robot (Unitree G1) targeted at ageing-care workflows (see Figure). The premise is simple: if humanoids are to be accepted in healthcare environments, they must be able to work safely near people, handle everyday objects reliably, and degrade gracefully when perception or communication is imperfect, without placing an unreasonable cognitive burden on staff. The work will be organised around a small set of clinically relevant, low-risk, high-frequency tasks that map directly to care settings, including safe object handover, item retrieval from shelves/trolleys, tray handling, and rehabilitation session setup (e.g., positioning items, presenting tools, preparing a simple station).
These tasks will be implemented on the real G1, with performance judged against explicit safety and usability criteria. Technically, you will build an end-to-end pipeline:
(1) Practical perception + intent inference for care-like scenes. You will fuse multimodal signals (e.g., arm/hand motion cues, gaze and/or speech) with camera perception to infer target object, grasp choice, timing, and handover intent. Vision-language grounding will be used pragmatically: to link verbal instructions to objects/affordances, surface hazard cues, and provide interpretable feedback to the operator. Critically, you will quantify uncertainty and design gating, so autonomy only escalates when perception is reliable; otherwise, the system falls back to safe, operator-led behaviours.
(2) Skill learning and shared autonomy that staff can actually use. You will collect demonstrations using a natural, contact-free teleoperation interface, learn reusable primitives (reach, grasp, handover, fetch-and-carry, tray handling), and organise them into a compositional skill graph. Where appropriate, policies will be refined in simulation under the same safety constraints before transfer to hardware using domain randomisation/residual learning. The shared-control arbitration will be explicitly risk-aware, with transparent override and predictable behaviour around humans.
(3) Human-centred evidence, not just a demo. You will co-design evaluation protocols with ageing-care stakeholders and assess the system using: contact forces/stability margins, task success/time, learning curves, and standard human factors measures such as NASA-TLX and acceptance ratings, complemented by short qualitative feedback. Ethics, accessibility and data governance will be embedded from the start to make outcomes credible for translation.
Outputs will include a reproducible G1 digital twin + safety layer, multimodal interaction datasets (where appropriate), and a benchmark task suite with evaluation protocols and demonstration videos.
General eligibility criteria: Applicants would normally be expected to hold a minimum of a UK Honours degree at 2:1 level or equivalent in a relevant degree course.
Project specific criteria: We welcome applicants from Engineering, Computer Science, Natural Sciences or related disciplines. Essential: strong programming skills (especially Python) and Motivation to work across robotics + AI (hands-on, iterative, experimental). Desirable: experience with ROS2/robot simulation, control, machine learning, perception, or human-centred experimentation. You should be motivated by real-world impact in healthcare and committed to safe, inclusive research practice.
Studentship funding: A tax-free stipend will be paid at the standard UKRI rate; £20,780 in 2025/26. This is a fully funded studentship of 3.5 years for UK/Home students.
Enquiries: Interested applicants are welcome to get in touch to learn more about the PhD project. Please contact Dr Ziwei Wang (z.wang82@lancaster.ac.uk) for more information.
Dates
Deadline for candidate applications: 23rd March 2026
Provisional Interview Date: April 2026
Start Date: October 2026
Further reading:
D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control Barrier Function Based Quadratic Programs for Safety Critical Systems,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3861–3876, 2017.
D. Argall, S. Chernova, M. Veloso, and B. Browning, “A Survey of Robot Learning from Demonstration,” Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469–483, 2009
G. Billard, S. Calinon, and R. Dillmann, “Learning from Humans,” in Springer Handbook of Robotics (2nd ed.), B. Siciliano and O. Khatib, Eds. Springer, 2016, ch. 74, pp. 1995–2014.
Driess et al., “PaLM-E: An Embodied Multimodal Language Model,” in Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR, vol. 202, pp. 8469–8488, 2023.
Zitkovich et al., “RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control,” in Proceedings of The 7th Conference on Robot Learning (CoRL), PMLR, vol. 229, pp. 2165–2183, 2023.
G. Hoffman, “Evaluating Fluency in Human–Robot Collaboration,” IEEE Transactions on Human-Machine Systems, vol. 49, no. 3, pp. 209–218, 2019.
You will receive a generic acknowledgement in receipt of successfully sending the application documents.
Please note that only applications submitted as per these instructions will be considered.
Please note that, if English is not your first language, you will be required to provide evidence of your proficiency in English. This evidence is only required if you are offered a funded PhD and is not required as part of this application process.
SATURN-CDT PhDs
The EPSRC Centre for Doctoral Training (CDT) in Skills And Training Underpinning a Renaissance in Nuclear (SATURN) is a collaborative CDT involving the Universities of Manchester, Lancaster, Leeds, Liverpool, Sheffield and Strathclyde will work towards building the skills base needed to support the UK’s net zero targets. Please see the listed Lancaster-based PhD opportunities below:
How to apply
Step 1
To register your interest in a PhD opportunity, please email the relevant project supervisor with your contact details and a comprehensive CV. Please also include a covering letter, if requested in the advert details.
Step 2
The project supervisor will contact you and may invite you to hold a Skype or telephone interview. At this stage, you can apply for more than one advertised project if you wish.
Step 3
If you are successful at interview for the studentship, you will be invited to apply via the admissions portal online. This will ensure that you receive a formal offer of admission. Please submit one application only, and state the studentship that you have applied for in the source of funding section.
Step 4
Once we have made a formal offer, you will need to check the conditions in your offer letter and supply any outstanding documents by the required deadlines. If your offer is unconditional, then this will not apply to you.
Other methods of applying for a PhD
Studying for a research degree is a highly rewarding and challenging process. You'll work to become a leading expert in your topic area with regular contact and close individual supervision with your supervisor.
If you have your own research idea, we can help you to develop it. To begin this process you will need to find a PhD Supervisor from one of our research groups, whose research interests align with your own.
