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 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.

About the Project

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.

Information about the Cockcroft Institute can be found at https://www.cockcroft.ac.uk/

Potential applicants are encouraged to contact Dr. Rosa Letizia (r.letizia@lancaster.ac.uk), Prof. Graeme Burt (g.burt1@lancaster.ac.uk ) or Prof. Rob Appleby (Robert.Appleby@manchester.ac.uk ) for more information.

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 at https://www.cockcroft.ac.uk/education/phd-opportunities/ .

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.

Contact for further information: Dr. Rosa Letizia r.letizia@lancaster.ac.uk

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