Computational Framework for Sustainable Materials, Manufacturing, Energy, and AI Driven Simulations

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying. 

The ongoing global pursuit of sustainable solutions in materials, manufacturing, and energy sectors has propelled the need for innovative computational frameworks that leverage the power of artificial intelligence (AI) to drive efficient simulations and innovations. My group’s research is at the intersection of physics-based and AI-driven simulations in solids and structures. This PhD research project offers an exciting opportunity to explore and develop a comprehensive Computational Framework to achieve sustainable solutions on ANY of the research areas given below.

•       Materials
•       Manufacturing
•       Energy
•       AI-Driven Simulations

The project's primary aim is to address the multifaceted challenges associated with achieving sustainability across industries. By integrating advanced computational techniques, data-driven AI algorithms, and domain-specific expertise. Depending on your choice of research area from above, this research will pave the way for transformative advancements given below.

Sustainable Materials: This research will focus on the development of eco-friendly materials through computational modelling, enabling the discovery of novel materials with enhanced properties and reduced environmental impact. Applications include materials for renewable energy, lightweight structures, and green materials.

Sustainable Manufacturing: The project will explore and dive into optimising manufacturing processes through Physics informed AI-driven simulations, reducing waste, energy consumption, and emissions while enhancing product quality.

Sustainable Energy: Sustainable energy solutions are pivotal in the fight against climate change. This research will explore advanced simulations to optimise the design and operation of renewable energy systems, and energy storage technologies for increased efficiency and reduced environmental impact.

AI-Driven Simulations: The project will extensively leverage artificial intelligence and machine learning techniques to enhance the accuracy and speed of simulations in all areas mentioned above. This will involve developing predictive models and harnessing the full potential of data-driven approaches for informed decision-making.

The proposed PhD research will be conducted at the intersection of materials science, manufacturing engineering, energy systems, and artificial intelligence, providing a unique opportunity to bridge gaps between traditionally distinct fields. It will involve a multidisciplinary approach, combining expertise in computational modelling, data analysis, and domain-specific knowledge to tackle the sustainability challenges of our time.

These projects offer an exciting and forward-thinking opportunity for PhD candidates to contribute significantly to the development of sustainable solutions that will shape the future of materials, manufacturing, and energy industries. The research will not only advance scientific understanding but also play a crucial role in addressing global sustainability goals and ensuring a greener, more sustainable future for generations to come.

Essential Background:

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Mechanical engineering, or materials science or relevant discipline.

Desirable knowledge:

An Engineering or Applied physics background with knowledge of CAD and finite-element-based modelling, and computer programming preferably with machine learning.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Engineering (PhD) to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you DO NOT need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at pgrs-admissions@abdn.ac.uk