Digital Twins of Hydrogen Nanobubble Generation System

We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2023. Applications are invited for the project Digital Twins of Hydrogen Nanobubble Generation System

Successful applicants will receive an annual stipend (bursary) of approximately £19,668, including inner London weighting, plus payment of their full-time home tuition fees for a period of 42 months (3.5 years).

You should be eligible for home (UK) tuition fees there are a very limited number (no more than three) of studentships available to overseas applicants, including EU nationals, who meet the academic entry criteria including English Language proficiency.

You will join the internationally recognised researchers in the Department of Chemical Engineering research and PhD programmes | Brunel University London

The Project

This exciting research project is focused on storing hydrogen nanobubbles in liquid carriers to produce hydrogen nanofuels is an emerging technique. This work proposes the development of digital twins to address the stability and controllability issues in nanobubble generation process. The digital twins will fuse first-principle models with data-driven models to describe the behaviour of hydrogen nanobubbles in various liquid carriers. The ultimate goal of this project is to achieve the generation of highly uniform and stable hydrogen nanobubbles, allowing for precise process control and optimization.

Please contact Dr Yang Yang at Yang.Yang@brunel.ac.uk for an informal discussion about the studentships.

Eligibility

Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science, Design, Mathematics, Physics or a similar discipline. A Postgraduate Masters degree is not required but may be an advantage.

Skills and Experience

Applicants will be required to demonstrate the following skills;

Machine learning: This project will require machine/deep learning techniques (e.g. decision trees, neural networks) for data-driven modelling using Matlab or Python.
Computational modelling: This project will require simulation modelling and optimisation techniques using Matlab or Python.

You should be highly motivated, able to work independently as well as in a team, collaborate with others and have good communication skills.

How to apply

How to Apply

There are two stages of the application:

1.Applicants must submit the pre-application form via the following link https://brunel.onlinesurveys.ac.uk/epsrc-dtp-23-24-pre-application-form-brunel-university-lon-3 by 16.00 on Friday 26th May 2023.

2.If you are shortlisted for the interview, you will be asked to email the following documentation in a single PDF file to cedps-studentships@brunel.ac.uk within 24hrs.

Your up-to-date CV;
Your Undergraduate degree certificate(s) and transcript(s) essential;
Your Postgraduate Masters degree certificate(s) and transcript(s) if applicable;
Your valid English Language qualification of IELTS 6.5 overall (minimum 6.0 in each section) or equivalent, if applicable;
Contact details for TWO referees, one of which can be an academic member of staff in the College.

Applicants should therefore ensure that they have all of this information in case they are shortlisted.

Interviews will take place in June 2023.

Meet the Supervisor(s)

Yang Yang - Dr. Yang is a Lecturer in Chemical Engineering Department. She is currently leading the Digital Manufacturing Group, which aims to integrate the advanced computational technologies, such as big data, machine/deep learning, simulation and visualisation, to facilitate manufacturers achieve tangible improvements in key metrics. Her multidisciplinary research spans across diverse industrial sectors, addressing the challenges and driving the industry towards a new revolution. Dr. Yang has a multidisciplinary background. She obtained her BSc and MSc degree in Computer Science from Tianjin University, China and received her PhD sponsored by Overseas Research Scholarships (ORS) and Tetley & Lupton Scholarships (TLS) from University of Leeds. During her PhD, she successfully applied data mining and machine learning techniques to identify the optimal composition of nano-photocatalyst (TiO2). The decisional tool designed and developed by Dr. Yang, which combined process analytical technology (PAT), image analysis and machine learning techniques, was sponsored and adopted by GlaxoSmithKline Pharmaceuticals (GSK) for its nanoparticle product line. Due to her outstaning performance, Dr.Yang was awarded Chinese Government Award for Outstanding Self-financed Students Abroad in 2010. Prior to joining Brunel, Dr. Yang worked at Imperial College London and University College London as a postdoctoral researcher. During this period, Dr. Yang accumulated great knowledge and experience in biopharmaceutical manufacturing process and personalised medicine development. Collaborated with UCB and Eli Lily, the leaders of biopharmaceutical industries in UK, Dr. Yang established process models and ecnomic models of biomanufacturing process using discrete-event modelling and Monte Carlo simulation methods. A decision-support tool which combined the process models, ecnomic models and machine learning models for facility fit analysis had been greatly complimented by biopharmaceutical industry users. Supported by Pall Corporation, Merck and Medimmune, Dr. Yang’s research of digital twins for continuous biomanufacturing process awarded funding by Future targeted healthcare manufacturing hub at UCL. She is currently holding Brunel Research Initiative & Enterprise Funding for digital twin system of hydrogen production. Multi-omics data analysis for personalised medicine development is another research intrest of Dr. Yang. She led a collaboration with Shanghai Pulmonary Hospital (China) to construct a decision-support tool with big data analysis for personalized diagnosis and treatment of lung cancer. She is currently collaborating with Life Science Department for cancer and drug dependency analysis.