AI for additive manufacture of complex flow devices
The Faculty of Science Doctoral Training Centre (DTC) in Artificial Intelligence (AI) is a new initiative by the University of Nottingham to train future researchers and leaders to address the most pressing challenges of the 21st Century, through foundational and applied AI research on a cohort basis. The DTC training and supervision will be delivered by a team of outstanding scholars from different disciplines cutting across arts, engineering, medicine and health sciences, science, and social sciences.
Project overview
Start date: 1 October 2026 for 3.5 years (42 months)
Medicines shortages have reached record highs in recent years due to adherence to logistically complex, labour intensive and inefficient batchwise manufacturing. High energy consumption and use of hazardous solvents cause environmental concerns, with long supply chains leading to extensive carbon footprints. This studentship will be part of a broader vision to leverage the latest advances in 3D printing and AI-driven design optimisation to enable on-demand medicines manufacture that is highly cost-efficient and sustainable.
Building on our previous work you will exploit recent advances in generative AI including diffusion‑based models. You will optimise a) reactor geometry to improve mixing and catalyst accessibility and b) process control parameter data (flow rate, temperature etc.), to enable highly efficient product synthesis. You will generate large computational model datasets, validated against the provided latest experimental data, to enable effective training and benchmarking of the AI models. Design optimisation will be driven by performance metrics guided by industrial partners from aligned grants, including Johnson Matthey and GSK. The project will equip you with a broad array of advanced computational and AI-based skills, experience of applying those methods to optimise real-life biotechnology, industrial contacts, and interdisciplinary knowledge suitable for a career in academia or industry.
The studentship is aligned with several current priority and proposed research streams within the Faculties of Computer Science and Engineering ensuring Post-Doctoral Research Associate (PDRA) expertise will be available to support you. This includes the current large EPSRC Programme Grant ‘Dial Up’ (PG-‘Dial Up’ - EP/W017032/1), where one of three product objectives is a 3D printed flow reactor. The broad and thriving environment of students, researchers and academics will provide you with the support, mentorship, skills and training to become an independent expert in AI optimisation of 3D printed technologies with applications related to medicines synthesis and healthcare technology.
Supervisors
- Dr Simon Attwood - Faculty of Engineering
- Professor Ricky Wildman - Faculty of Engineering
- Professor Ender Ozcan - School of Computer Science
- Dr Mirco Magnini - Faculty of Engineering
Candidate requirements
We are seeking an enthusiastic and motivated candidate with a strong quantitative background and a passion for AI driven scientific discovery, ideally with interest in geometric modelling, 3D structures, or design automation.
Minimum of a 2:1 bachelor's degree in a relevant discipline to the research topic - please consult with the potential supervisors. See the "how to apply" section for contact details.
Read our application guide for full guidance on residency, qualifications and English language requirements.
Essential skills
- Degree in Computer Science, Engineering, Artificial Intelligence, Applied Mathematics, or a related discipline
- Strong programming skills
- Familiarity with machine learning, especially deep learning frameworks
- Good understanding of algorithms, numerical methods, or scientific computing
Desirable skills
- Experience with generative models
- Knowledge of (surrogate) optimisation algorithms, or (meta)heuristics
- Background in computational fluid dynamics (CFD) or simulation based modelling (not essential; training will be provided)
- Familiarity with high performance computing (HPC) environments.
Funding and eligibility
This studentship is open to UK/home applicants only.
Annual tax-free stipend based on the UKRI rate (£21,805 for 2026/27), home tuition fee, and a £3,000 p.a. Research Training Support Grant.
How to apply
Application deadline: Sunday 19 April 2026. You must have completed and submitted your application to the NottinghamHub system by this date.
Read our application guide for full guidance on how to apply. The application process has two steps.
Email Dr Simon Attwood (Faculty of Engineering) for further details and to arrange an interview.