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Large-scale Scientific Machine Learning for the Energy sector (Distributed Algorithms CDT)

   Department of Electrical Engineering and Electronics

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  Dr Navjot Kukreja  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training programme that will equip up to 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.

In this project, we will aim to build large models that combine Partial-Differential Equation solvers with Deep Learning. The project involves development at all levels of the stack, from designing a high-level interface that captures details of both the PDE solver and the Deep Learning models, to combining them to achieve high performance when distributed across a large computational cluster (possibly on a cloud system), and implementation of common HPC programming patterns to achieve cutting-edge performance while training neural networks that are today considered too large to be trained.

RocketML is a company dedicated to providing high-performance machine learning solutions to its clients. RocketML wishes to use this project to inform the development of their offering to problems in several deep-tech sectors including energy, manufacturing, and pharmaceuticals.

We will start by identifying some key benchmark problems that combine PDE solvers and DL models in a meaningful way. The student will then explore the development of a high-level interface that can capture the details of both aspects of the model in such a way that they can be analysed by an underlying compiler in a combined fashion. Then the student will move to the lower-level implementation that will power the high-performance large-scale models that this method is intended to enable.

Students will be based at the University of Liverpool and will be part of the CDT and Signal Processing  research community - a large, social and creative research group that works together solving tough research problems. Students have two academic supervisors and an industrial partner who provide co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.

The CDT is committed to providing an inclusive environment in which diverse students can thrive. The CDT particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector. We can also consider part time PhD students. We also encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference. 

The studentship is open to Students Worldwide.

Funding Notes:

Visit the CDT website for funding and eligibility information.

You must enter the following information:

·      Admission Term: 2022-23

·      Application Type: Research Degree (MPhil/PhD/MD) – Full time

·      Programme of Study: Electrical Engineering and Electronics – Doctor in Philosophy (PhD)

The remainder of the guidance is found in the CDT application instructions on our website.

Contact the supervisors (named above) in the first instance or visit the CDT website for Director, Student Ambassador and Centre Manager details.

Name and email address to direct enquiries to:

Application Web Address:

Visit the CDT website for application instructions, FAQs, interview timelines and guidance.

Tel. No. for Enquiries:

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