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PhD Studentship : Artificial Intelligence (AI) & Inverse Modelling for Welding

University of Nottingham - Science

Qualification Type: PhD
Location: Nottingham, Hybrid
Funding for: UK Students
Funding amount: See advert for details
Hours: Full Time
Placed On: 7th February 2024
Closes: 15th March 2024
Reference: SCI258

This project is an exciting opportunity to undertake industrially linked research in partnership with the Manufacturing Technology Centre (MTC). It is based within the School of Mathematical Sciences at University of Nottingham which conducts cutting edge research into machine learning methods for inverse modelling problems. 

This is 3-year fully funded studentship is only open to UK home students. The successful applicant will receive a generous tax-free annual stipend of £26,658 plus payment of their full-time home tuition fees. Due to funding restrictions this PhD position is only available to UK nationals. As this position is sponsored by the MTC, any successful candidate would need to pass the sponsors own security checks prior to the commencement of the PhD.

Vision

We are seeking a motivated PhD candidate with enthusiasm to learn about state-of-the-art developments in machine learning and AI algorithms for inverse problems arising from mechanistic models for welding. Mechanistic models provide a rich, principled and physics constrained approach to simulate welding of different materials, of different joint geometries, under different assumed conditions. However, training and examining the configurations of such mechanistic models using experimental data and be a cumbersome and intractable task. In this project, we study how advances in AI can be utilized to provide efficient mechanisms for improving mechanistic welding processes directly from observed data.

Motivation 

In critical manufacturing sectors, such as aerospace and nuclear industries, there is a challenge to achieve precise and reliable results in laser processing such as welding due to variations in material characteristics, joint geometries, and environmental factors. Conventional methods often rely on manual adjustments and trial-and-error, leading to suboptimal welding quality and increased production cost/time. This project would focus on investigating the use of artificial intelligence (AI)-driven inverse modelling to iteratively fine-tune laser parameters (e.g., power, focus, speed) and material properties (e.g., thickness, thermal conductivity) based on desired outcomes, such as joint integrity, minimal distortion, residual stress or weld quality. 

Aim

You will have the opportunity to join a multidisciplinary team of supervisors: experts in physics, engineering and fluid mechanics; experts in foundation computer science and mathematical foundations of AI and experts in the large scale welding simulator models. You will work alongside a team of research engineers based at the MTC, as well as, a vibrant cohort of PhD candidates from the School of Mathematical Sciences at University of Nottingham, the Horizon DTC and the AI DTC at the University of Nottingham.

Who we are looking for

An enthusiastic, self-motivated, PhD candidate with an aptitude for programming and problem solving. The ideal candidate would have (i.e. or expect to have by the start date) a 1st or a 2:1 degree in a STEM field such as Mathematics, Computer Science, Engineering, Physics and others. Prerequisite background in AI or inverse problems would be advantageous but it is not expected. However, we do expect candidates to have adequate experience in coding in at least one object oriented language (Python, MATLAB, R, C++ etc.). 

Applications and enquiries can be made informally directly to the supervisor (Dr. Yordan P. Raykov) at yordan.raykov@nottingham.ac.uk, but post interview application to be made through MyNottingham system.

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