PhD Studentship sponsored by CERES Industrial Consortium on Artificial Intelligence tools for Accelerated Performance Predictions and Design in Compressor Systems

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

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 . 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.

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.

For further information about the post please contact [email protected]  

Applications, consisting of a CV and a Personal Statement, should be submitted to [email protected]

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

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