Founded in 1894, City, University of London is a global university committed to 
academic excellence with a focus on business and the professions and an enviable 
central London location.

City attracts around 20,000 students (over 40% at postgraduate level), from more 
than 150 countries and staff from over 75 countries.

In the last decade City has almost tripled the proportion of its total academic 
staff producing world-leading or internationally excellent research. During this 
period City has made significant investments in its academic staff, its estate 
and its infrastructure and continues to work towards realising its vision of 
being a leading global university.

Background 
This PhD studentship is funded by CERES - Industrial Consortium for Compressors 
and Expanders in Future Energy Systems.
https://blogs.city.ac.uk/ceres/

The Centre for Compressor Technology started the Industrial Consortium to create 
a network of partners for addressing global challenges by performing world-
leading research in compression and expansion technologies for future energy 
systems and expanding the scope by sourcing funds from research councils.

http://researchcentres.city.ac.uk/thermo-fluids/compressor-technology

The multidisciplinary PhD project, entitled 

“Artificial Intelligence tools for accelerated performance predictions and 
design in compressor systems”,

is focused on creating a smart tool that yields new blade profiles with 
specified performance metrics and operating and manufacturing constraints. The 
key outcomes of the proposed project are an alternative AI-based analysis tool 
for performance analysis of compressor systems and a first-of-its-kind smart 
generative tool for novel rotor designs using AI.

Responsibilities 
The PhD student will work closely with the world leading experts in applied 
machine learning, artificial intelligence and modelling of rotating machinery 
under the Chair. The overall objective is to realise a smart tool that yields 
new blade profiles with specified performance metrics and operating and 
manufacturing constraints and validate these methods with the experimental 
results obtained in the state-of-the-art laboratory within the Centre for 
Compressor Technology and the Thermo-Fluids Research Centre.

Person Specification 
It is expected that the candidate has a good mathematical background, experience 
in artificial intelligence, knowledge of thermodynamics and fluid mechanics, has 
good skills in using programming languages such as Python or similar. A Master’s 
degree in mechanical engineering or related discipline with prior experience in 
using machine learning tools such as Tensorflow/Keras is advantageous. The 
candidate is expected to have a positive attitude to teamwork, ability to work 
proactively and independently and has motivation to learn and contribute to this 
multidisciplinary project.

Closing date for applications: 17:00 on 25th June 2022

Salary: £17.5K per year, 3 years plus travel budget.

Interviews will be held week commencing 27th June 2022

The role is available from July 2022

Actively working to promote equal opportunity and diversity

Academic excellence for business and the professions