Research Associate/Assistant – Developing AI-Driven Models

We invite applications for a Research Associate/Assistant position focused on developing an advanced AI-based platform. This platform is designed to assist designers in rapidly evaluating the manufacturability of sheet stamping components, ensuring optimal design and material selections. This role is part of the exciting 'AI-Driven Automotive Material Selection and Structural Design for Manufacturing' project, funded by Innovate UK Smart Grants. The project aims to address real industrial challenges through collaboration with industrial partners.

As the successful candidate, you will join a multidisciplinary team with expertise in design engineering, manufacturing, and software development. You will lead the design and development of Artificial Intelligence (AI) models, including a material encoding model and a manufacturability surrogate model, using advanced deep-learning neural networks. This role is critical in delivering a disruptive solution that utilises AI techniques for the instantaneous design and optimisation of lightweight, efficient vehicle components.

• Define deep learning neural network architectures for the material encoder and manufacturability surrogate AI models.


• Prepare datasets by processing material and stamping simulation data, provided by an industrial partner, into AI-compatible formats.


• Train, test, and evaluate the combined material model and manufacturability surrogate model.


• Support the industrial partner in integrating the developed dual AI model framework into their software backend and test the integration.


• Assist with tasks related to software platform development and user testing, mainly undertaken by the industrial partner.


• Ensure the validity and reliability of data at all times.


• Maintain accurate and complete records of all findings.


• Manage data effectively.


• Write or assist in writing reports for research sponsors.


• Submit publications to refereed journals and/or conferences.


• Assist with the supervision of undergraduate and postgraduate research students as required.


• Assist with technology exploitation and dissemination, including participation in conferences, showcases, workshops, publications, industrial events, and outreach activities.


• Develop contacts within the College and the wider community.


• Promote the reputation of the Group, the Department, and the College.

• PhD, or near finishing their PhD, in a relevant subject such as computing, data science, mathematics, design engineering, mechanical engineering, electrical and electronics engineering, aeronautical engineering, materials, etc. Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.


• AI in engineering, more specifically, developing deep learning neural networks and processing scalar/image/graph-based databases.


• Practical experience within an interdisciplinary research environment and publishing in refereed journals/conferences.


• Knowledge of data-driven modelling, deep learning (such as CNN), and optimisation.


• Knowledge of research methods and statistical procedures.


• Skilled in working in a multidisciplinary environment and effectively collaborating with other researchers / subject experts from other areas for interdisciplinary research.


• Skilled in Programming using Python and MATLAB.


• Ability to conduct a detailed review of recent literature.

Please refer to the job description for a full list of the essential and desired requirements.

This is a full-time (FTE of 1.0) role for 6 months.

Should you require any further details on the role please contact: Dr Nan Li [email protected].

Any queries regarding the application process should be directed to Monika Delczyk at [email protected].

Our preferred method of application is online via our website. Please click ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/ and search using reference number ENG0  Please complete an application form as directed.

Further information about the post is available in the job description.