PhD in Deep Learning for Autonomous Digital Manufacturing

Project Overview

Machine learning/deep learning (ML/DL) is an important enabler for advancements of modern smart factory automation driven by web of distributed sensing, communications, and control intelligence. This progress is often dubbed as industry 4.0 (following the previous big advances of steam power, the assembly line; and, early automation).

Research and innovation in several fields, not least of these the underlying e-mobility manufacturing technology developed by WMG, provides the momentum for Industry 4.0 and its successes in productivity, efficiency, precision, flexibility and safety.

One of the addressed challenges is focus on web of distributed sensing strategies to be synergistically linked with machine learning approaches to achieve near-zero-defects manufacturing systems, i.e., ideally, the system works as intended all the time and the process never fails. This challenge requires advancements on two levels as related to: (i) web of distributed sensing which can be used as and when needed and delivered by for example Unmanned Aerial Vehicle (UAVs) (aka drone), and (ii) ML/DL algorithm

incorporating learning from the strategically placed sensors/IoTs augmented by process-based information. We are inviting individual interested in research in ML/DL applied to manufacturing systems.

This is an excellent opportunity for you to apply your technical skills within the strategically important areas of machine learning/deep learning and its application for a new Autonomous Digital Manufacturing being developed as part of the new EPSRC Made Smarter Innovation - Research Centre for Smart, Collaborative Industrial Robotics (CESCIR) focused on creating a multi-disciplinary, cross-sectorial hub to set the national research agenda and WMG HVM Catapult Centre providing platform for industrial testbed and case studies within the e-mobility.

A talented individual with a background in machine learning/deep learning or/and mechatronics is being invited to join a team of multidisciplinary researchers (material/mechanical/industrial/computer engineers and computational modellers) to work on this research.

Essential and desirable criteria

Technical knowledge and experience in the areas of

1) machine learning/ deep learning and/or mechatronics

2) use of software packages such as Python or/and TensorFLOW, MATLAB, C++, R;

3) closed-loop control systems and/or software-in-the-loop simulations; and,

4) understanding of one manufacturing process.

Expected learning outcomes at the end of PhD:

1) Expert in ML/DL and relevant manufacturing application (for example, drone usage for inspection);

2) Expert in Deep Learning for Autonomous Digital Manufacturing

3) High track record with research output (at least 3 journal papers will be deemed by the end of the PhD);

4) understanding and knowledge about generating industrial impact.

Key Information:

Funding Source: DTP

Supporting company: EPSRC Made Smarter Innovation - Research Centre for Smart Cobotics

Supervisor: Prof Dariusz Ceglarek and Dr Pasquale Franciosa

Available to all students

Start date: June 2023

To apply

To apply please complete our online enquiry form and upload your CV.

Please ensure you meet the minimum requirements before filling in the online form.

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