Physics-informed machine learning for dynamic modelling and stochastic control

We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2024. Applications are invited for the project titled “Physics-informed machine learning for dynamic modelling and stochastic control”.

Successful applicants will receive an annual stipend (bursary) of £21,237, 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

The project aims to develop a framework that combines physics-informed machine learning techniques with stochastic predictive control for robust optimisation. Machine learning algorithms, such as Gaussian processes and recurrent neural networks, will be used to capture the underlying dynamics of complex systems while incorporating physics-based constraints and prior knowledge for improved accuracy and interpretability. By integrating stochastic predictive control algorithms, the framework will enable probabilistic decision-making and real-time optimisation in uncertain and dynamic environments.

This novel framework will be implemented and validated in mineral separation processes, such as froth flotation and gold leaching, to tackle the challenges arising from the growing demand for mineral resources to support the green energy transition. The PhD candidate will develop next-generation computer-based tools for modelling and control to be at the forefront of technological innovation, with real-world impact in the field of minerals processing.

Please contact Dr Paulina Quintanilla at paulina.quintanilla@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

Proficiency in MATLAB and Python is essential, as these tools are fundamental to the project's methodology. Familiarity with advanced optimisation libraries such as CasADi, multiphysics simulation platforms like COMSOL, and version control systems (e.g. GitHub) will be highly beneficial.

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

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-24-25-pre-application-form-brunel-university-lon

by 16.00 on Friday 5th April 2024.

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 72hrs.

• 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 April/May 2024.