PhD Project Description

The rapidly evolving field of microfluidics, in which fluids are manipulated at a microscopic scale, finds many applications in industries, ranging from the pharmaceutical to the oil industry. Most ubiquitous fluids are non-Newtonian and complex such as paints, blood, inks and personal-care products are just a few examples. It also raises curiosity to understand the behaviour of micro- and nano-particles in confinement and under flow that is widely used in various industrial applications, including drug delivery, cosmetics, food, medical diagnostics, environmental remediation, energy and agro-based industries. The technique of controlled manipulation of colloidal particles using microfluidics as a tool in lab-on-a-chip has unlocked opportunities to overcome many limitations of conventional technologies such as the requirement of multiple preparation steps, long processing times and large sample volumes.

The project aims to develop and optimise innovative strategies to control the motion and spatio-temporal distribution of micro/nano-particles in complex fluids in a microfluidic environment. The candidate will design and fabricate microfluidic devices and characterise the flow/particle interaction through optical microscopy methods. There will be exposure to several experimental techniques for the synthesis and characterisation of functional particles and undertaking proof-of-concept studies to identify prospective applications of the developed microfluidic systems - especially for drug delivery and point-of-care diagnostics.

This is an opportunity for a self-funded student, to start at a standard start date (April, October or January).

Informal enquiries and how to apply

For informal enquiries, please contact Dr Naval Singh n.singh1@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 Singh.

Lancaster University is inviting applications from undergraduate students for a full-time, paid summer internship supported through the UKAEA Fusion Internship Scheme. This is a UK-based, 8-week summer internship in a fusion-related discipline. The internship is intended to provide hands-on experience and skills development relevant to future careers, while increasing awareness of opportunities within the fusion sector.

Internship project

The internship will be hosted at Lancaster University within fusion-related robotics research activities. The successful student will contribute to a defined summer project relevant to robotics for challenging environments connected to fusion applications.

The project is expected to involve supervised work in areas such as:

• remote robotic operation and manipulation
• sensing, experimentation, and system testing
• data collection, analysis, and technical evaluation
• project documentation and research presentation

The internship is intended to provide real-life project experience in an active research environment, helping the student develop practical skills, technical confidence, and broader awareness of fusion-related engineering challenges.

At the end of the internship, the student will be required to produce a poster detailing their project, learning, and experience. The poster will be presented at a showcase event in September 2026 at UK Atomic Energy Authority, Culham.

Internship details

• Duration: 8 weeks
• Hourly rate: £14.24 including holiday pay
• Location: Lancaster University, UK
• Period: Summer 2026, starting from Monday June 1^st

Eligibility

Applicants must:

• be studying an undergraduate course at a UK university
• be returning to their studies after completion of the internship
• have the legal right to work in the UK and be able to receive public funds

How to apply

Please send the following to z.wang82@lancaster.ac.uk:

• a CV
• a short cover letter explaining your interest in the internship and any relevant experience
• your current degree programme and year of study

Please use the subject line:

Application – UKAEA Fusion Internship Scheme 2026

Application deadline: 25^th May 2026

For informal enquiries, please contact:

Dr Ziwei Wang
Lecturer in Robotics
Lancaster University
z.wang82@lancaster.ac.uk

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.

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. Optimisation 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 Parameterised 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.

Informal enquiries and how to apply

Informal enquiries to Dr M. Sergio Campobasso: m.s.campobasso@lancaster.ac.uk. Applications from UK applicants only should be made to: https://www.exageo.org/apply/ by Friday 22 May 2026.

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: 31st May 2026

Provisional Interview Date: June 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.

Application process 

1. Please complete the Natural Sciences Funded PhD Application Form 
   
   
2. Please submit your CV and two references to naturalsci@lancaster.ac.uk as Word or PDF files.
   
   
3. You will receive a generic acknowledgement in receipt of successfully sending the application documents. 
   
   
4. Please note that only applications submitted as per these instructions will be considered. 
   
   
5. 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.