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

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< img src="" class=" ff-og-image-inserted" > SALARY: ₤ 17.5 K each year, 3 years plus travel spending plan

Founded in 1894, City, University of London is an international university committed to scholastic quality with a concentrate on business and the professions and an enviable central London place.

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

In the last years City has nearly tripled the proportion of its total scholastic personnel producing world-leading or worldwide excellent research. Throughout this duration City has actually made significant investments in its academic personnel, its estate and its infrastructure and continues to work towards understanding its vision of being a leading worldwide university.


This PhD studentship is moneyed by CERES – Industrial Consortium for Compressors and Expanders in Future Energy Systems. The Centre for Compressor Technology started the Industrial Consortium to develop a network of partners for resolving global obstacles by carrying out world-leading research study in compression and expansion innovations for future energy systems and broadening the scope by sourcing funds from research councils.

The multidisciplinary PhD project, entitled “Artificial Intelligence tools for sped up performance forecasts and style in compressor systems”, is concentrated on creating a smart tool that yields brand-new blade profiles with specified performance metrics and operating and making restraints. The crucial results of the proposed project are an alternative AI-based analysis tool for performance analysis of compressor systems and a first-of-its-kind wise generative tool for unique rotor designs utilizing AI.


The PhD trainee will work closely with the world leading professionals in used artificial intelligence, expert system and modelling of turning equipment under the Chair. The overall objective is to realise a smart tool that yields brand-new blade profiles with specified efficiency metrics and operating and manufacturing restrictions and verify these techniques with the experimental results gotten in the advanced 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, understanding of thermodynamics and fluid mechanics, has good abilities in utilizing shows languages such as Python or similar. A Master’s degree in mechanical engineering or associated discipline with prior experience in utilizing machine learning tools such as Tensorflow/Keras is advantageous. The candidate is anticipated to have a positive attitude to teamwork, ability to work proactively and individually and has motivation to find out and add to this multidisciplinary job.

Additional Information — This area includes standard text. Please include the closing date that you need

Please see extra details about the role attached.

For additional information about the post please contact [email protected]!.?.! Applications, including a CV and a Personal Statement, mustbe submitted to Postgraduate Research Course Officer ([email protected]!.?.!). Closing date for applications: 17:00 on 25 th June 2022 Interviews will be held week starting 27 th June 2022 The role is available from July 2022Actively working to promote equal opportunity and variety

Academic excellence for company and the occupations Click Here to Apply For The Job



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