The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Catalysts have attracted growing interest due to their unique effects on chemical reactions, drug discovery and health care. In particular, catalysts materials innovation is the key for addressing Net Zero and Clean Growth challenges. Artificial intelligence has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates computational materials discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers but also those who work in materials science. As a result, the number of ML and artificial neural network (ANN) applications in materials innovation and pharmaceutic molecular design is growing at an astounding rate.
We would like to work with an enthusiastic PhD student to develop new algorithms for machine learning to screen and understand the structural factors for catalysis materials design, focusing on hydrogen generation and storage. At the end of their PhD, they should be able to produce high-quality codes and high-performance computing skills as well as contribute solutions to Net Zero and Clean Growth challenges. Developing new materials design methods and big data indexing innovations (from literature pools) to extract the new catalysis materials design principles will be encouraged as part of the student’s research and is an area for high-impact publication. This is not only a transdisciplinary project, but it also intends to foster a broader understanding of catalysis materials and AI data mining, which is fit for current challenges in energy technologies, from clean energy generation to low carbon footprint energy storage.
The student will gain fundamental knowledge in machine learning, state-of-the-art of catalysis science and technologies, as well as practical experiences in coding using high-performance computing facilities. Equipped with these skills, the student will be highly competitive and sought after both in industry and academia.
Start date: 1st October 2022
The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes:
- T1: data from large science facilities (particle physics, astronomy, cosmology)
- T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)
Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.
Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions.
Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 10 positions in 2022.
The partners include: We Predict, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, Intel, IBM, Microsoft, Quantum Foundry, Dwr Cymru, TWI and many more.
More information, and a description of research projects, can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website. http://cdt-aimlac.org/cdt-research.html
How to apply:
To apply, and for further details please visit the CDT website http://cdt-aimlac.org/cdt-apply.html and follow the instructions to apply online.
This includes an online application for this project at (with a start date of 1st October 2022): https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy)
Applicants should submit an application for postgraduate study via the Cardiff University webpages including:
• your academic CV
• a personal statement/covering letter
• two references, at least one of which should be academic
• Your degree certificates and transcripts to date.
In the "Research Proposal" section of your application, please specify the project title and supervisors of this project.
In the funding section, please select that you will not be self funding and write that the source of funding will be “AIMLAC CDT”
The deadline for applications for the UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) is 12th February 2022. However, AIMLAC will continue to accept applications until the positions are filled.
For general enquiries, please contact Rhian Melita Morris [Email Address Removed]
The typical academic requirement is a minimum of a 2:1 physics and astronomy or a relevant discipline.
Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (https://www.cardiff.ac.uk/study/international/english-language-requirements)
Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website http://cdt-aimlac.org/