Printing the un-printable: Machine Learning-driven 'Active' Slot-die Coating for the Precision Manufacture of Complex Functional Materials

About the Project

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.

We are looking for an ambitious researcher to lead the Materials 4.0 revolution by transforming passive manufacturing into intelligent, autonomous active processes for the fabrication of high-value novel coatings and devices. This fully funded studentship, starting in 2026, is part of the EPSRC Centre for Doctoral Training (CDT) in Developing National Capability in Materials 4.0. It offers a unique opportunity to bring together high-level Artificial Intelligence (AI) and machine learning, novel film sensing and thin metrology, with cutting edge slot-die coating manufacturing approaches.

The Mission: Beyond Passive Manufacturing

Slot-die coating is the premier technique for the continuous production of high-value functional thin films - the literal building blocks of next-generation photovoltaics, biosensors, and energy storage systems. However, traditional methods are 'passive' and 'open-loop'. Once the parameters are set, the system is left to the mercy of natural physical processes, often resulting in defects like "coffee-ring" effects, uneven drying resulting in non-optimum coatings and poor device performances.

Currently, materials science is moving so fast that we are developing 'hard-to-coat' inks that are functionally superior but practically 'un-printable' due to unstable rheology. We don't want to settle for easier-to-process materials; we want to build a system smart enough to handle the difficult ones.

The Project: Active Control and Digital Twins

In this project, you will move beyond passive deposition into the realm of 'Active Slot-Die Coating'. You will integrate real-time sensors and external stimuli - specifically electric fields and active flow control - into an automated coating line.

The primary hurdle isn't just the hardware; it’s the intelligence behind the control. Your role will be to:

• Engineer ML Control Algorithms: Develop AI models that act as a "digital twin," interpreting multi-fidelity sensor signals to identify priority feedback for live, autonomous adjustments.
• Smart Metrology: Design "information-rich" imaging systems that move characterization from a "post-mortem" analysis to a live, in-situ process.
• Bridge the Gap: Use ML to bridge the gap between hydrodynamic simulations and the "noise" of real-world manufacturing.

Who are we looking for?

This project would suit someone from an Physical Science and Engineering background and your application is encouraged. We are particularly keen to receive applications from candidates with an enthusiasm for Machine Learning, Computer Science, or Control Systems Engineering. It is important to note you don’t need to be an expert in thin film processing on Day 1, but you must be:

• Curious and Engaging: We want someone who is ready to dive into the "physics" of the problem while bringing their computational expertise to the table.
• Willing to Learn: You will be supported in mastering the technical and engineering aspects of slot-die coating and thin-film deposition via in-house knowledge and extensive interactions and training from the industrial partner.
• Interdisciplinary: You will be part of a world-class supervisory team, including Prof. Jonathan Howse (Coating Trials), Dr. Morgan Jones, and Prof. George Panoutsos (ML/AI and Computational Intelligence).

World-Class Industry and Global Impact

This research is grounded in commercial reality through an industrial partnership with FOM Technologies (Denmark). As part of your training, you will:

• Attend the FOM Coating School to master the physics of Roll-to-Roll (R2R) processing.
• Gain access to specialized R2R equipment at Echion Technologies in Cambridge for rapid prototyping.
• Go Global: This project includes a proposed 3-6 month sabbatical at Argonne National Labs (USA), working with a consortium of US national laboratories on high-TRL energy challenges.

Training and Capability Development

As a member of the Materials 4.0 CDT, you will receive a comprehensive suite of academic and practical training:

• Advanced Modules (@ Uni Sheffield) in subjects like Computational Intelligence (ELE428), Data Modelling (ELE448), and Industrial Automation (ELE426).
• Specialized Skills: Develop expertise in LabVIEW I/O systems for bespoke instrumental control, on-line metrology, and COMSOL Multiphysics for digital twin modelling.
• Professional Growth: Benefit from peer-to-peer learning and ethics in AI within a supportive CDT cohort.

Commitment to Inclusivity

We are dedicated to making this project accessible. Because of its strong digital and computational focus, the project can be pragmatically adjusted to suit researchers with accessibility requirements. This includes pivoting toward Computational Fluid Dynamics (CFD), remote monitoring, and data-centric methodologies where you can act as a 'Lead Analyst' from an accessible workstation.

Enquiries

For general enquiries, please contact (doctoral-training@royce.ac.uk)

For application-related queries, please contact Rebecca Milner (rebecca.milner@sheffield.ac.uk).

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Prof Jonathan Howse (j.r.howse@sheffield.ac.uk).

Application Process

Please note that each partner of the CDT in Materials 4.0 will have its own application process. The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups.

Application Web Page

https://www.sheffield.ac.uk/postgradapplication/login.do

After the personal details, you need to 'add research course', and select 'Doctoral Training Course', and then 'Developing National Capability for Materials 4.0'.