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Overview

Applications are invited from enthusiastic researchers to participate in an exciting program of research on developing advanced modelling for thermal hydraulics analysis of nuclear reactors making use of AI/machine learning. The project is part of the development of computer tools and methods of a UK consortium, Collaborative Computational Project in Nuclear Thermal Hydraulics (CCP-NTH) https://ccpnth.ac.uk/, one of the CCPs funded by the research councils.

One of the key challenges in modelling reactor systems lies in their inherently multi-scale and multi-physics nature. Fully resolved CFD modelling of the entire reactor cores are not feasible in the foreseeable future due to computational constraints. However, under-resolved and coarse-grid CFD models can play a crucial role, especially when coupled with system-level approaches and locally resolved CFD. In this context, AI/ML presents opportunities to enhance sub-models used in low-resolution simulations, while heterogeneous computing and accelerators may offer pathways to simulate large systems with improved fidelity. The current project is to explore such prospects. Following a careful assessment of different potential methodologies, the project will be aimed at developing improved modelling for under-resolved or coarse-grid CFD making use of AI/machine learning approaches, while leveraging new understanding of flow physics and/or using new coarse-grid CFD approaches.

You should hold or are about to complete a PhD in computational fluid dynamics, nuclear thermal hydraulics, machine learning or have equivalent experience. Candidates should have a good background and experience in one or more of the following areas: nuclear thermal hydraulics, programming, turbulence, CFD, AI/machine learning and a good understanding of flow physics.

Main duties and responsibilities

• Carry out a review of applications of AI and machine learning in reactor core thermal hydraulics analysis noting the wider application in CFD in general.
• Work towards developing improved modelling for under-resolved or coarse-grid CFD making use of AI/machine learning approaches, while leveraging new understanding of flow physics such as Apparent Reynolds number theory or using the sub-channel CFD approach.
• Produce a report summarising research and literature review findings and recommendations for future research, which will form part of the final report to be submitted to the funder.
• Support other work packages of the CCP-NTH project when/as required.
• Collaborate with other team members of the CCP-NTH team based at STFC Daresbury Laboratory.
• Publish conference and journal papers on the work.
• Present the work at national and international meetings and conferences.
• Disseminate the project outcomes to potential users and industrial collaborators, and to the wider scientific community of researchers.
• Attend regular project meetings and present own results.
• Work closely and communicate well with all project partners about research progress and progress towards deliverables.
• Carry out other duties, commensurate with the grade and remit of the post.

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

Essential criteria

• A good honours degree in Engineering/computer science/mathematics /physics or an equivalent discipline. Essential/Interview Application.
• A PhD in computational fluid dynamics, nuclear thermal hydraulics or machine learning, or other relevant field.  Essential/Interview Application.
• Significant experience in one or more of the following areas: nuclear thermal hydraulics, programming, turbulence, CFD, AI/machine learning and a good understanding of flow physics.  Essential/Interview Application.
• Effective communication skills, both written and verbal; Technical report and paper writing skills, experience of delivering presentations.  Essential/Interview Application.
• Ability to develop creative approaches to problem solving.   Essential/Interview Application.
• Ability to work within a University research group and in collaboration with external partners.  Essential/Interview Application.
• Ability to analyse and solve problems with an appreciation of longer-term implications.  Essential/Interview Application.
• Ability to plan and progress work activities.  Essential/Interview Application.
• Ability to learn new skills quickly and effectively.  Desirable/Interview Application.
• Experience of developing and maintaining a network of contacts throughout own work area. Desirable/Interview Application.
• Experience of adapting own skills to new circumstances.  Desirable/Interview Application.

Desirable criteria

• Experience of developing and maintaining a network of contacts throughout own work area. Desirable/Interview Application.
• Experience of adapting own skills to new circumstances.  Desirable/Interview Application.

Further Information

Grade: 7

Duration: 18 Months

Line manager: Professor Shuisheng He

Direct reports: N/A

For informal enquiries about this job contact
Professor Shuisheng He on s.he@sheffield.ac.uk or on 0114 227756

Next steps in the recruitment process

It is anticipated that the selection process will take place within three to four weeks of application closing date. This will consist of an interview and a tour of the department if required. We plan to let candidates know if they have progressed to the selection stage two weeks after the closing date. Contact Lisa Gardiner on l.m.gardiner@sheffield.ac.uk if you require any reasonable adjustments.

By applying, you are agreeing that we can share your information with an external collaborator at the Science and Technology Facilities Council.

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).
 

What we offer

• A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways
• A range of generous family-friendly policies
  • paid time off for parenting and caring emergencies
  • support for those going through the menopause
  • paid time off and support for fertility treatment
  • and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.