Senior Research Associate in Data Science for Materials Engineering

A senior postdoctoral position is available to deliver research activities on machine learning to develop surrogate models (emulators) of complex structural materials models. This role is being advertised as part of a five-year project (commenced in May 2021) called SINDRI . SINDRI’s aim is to deliver a step change in the design and assessment of structural materials deployed in nuclear power plants using easy to use yet accurate models. The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) and EDF.

This is an exciting opportunity for a postdoctoral researcher, with experience in applying machine learning techniques to physical models and simulation of solids. You will be collaborating with a wide range of academic and industrial partners (including but not limited to EDF, UKAEA, Rolls-Royce, Jacobs, and NNL) and work on projects with major national and industrial relevance.  It will allow them to participate in world-leading research in the Solid Mechanics Research Group (SMRG ) within the School of Electrical, Electronic and Mechanical Engineering (EEME) at the University of Bristol.

Other researchers in the project will develop multi-physics models to simulate the fabrication and in-service behaviour of representative alloy systems and validate them against high fidelity experiments such as synchrotron diffraction. Your roll will be to produce surrogate models of those physics-based models using techniques such as Gaussian Process, neural network, or polynomial chaos expansion to bridge the gap between different length-scales. The easy-to-use and fast emulators will be used by engineering practitioners at our industrial partners.

Research Responsibilities

• Deliver a portfolio of surrogate modelling
• Liaise closely with researchers at partners universities who carry out experiments and develop models
• Coordinate research activities and objectives with industrial partners, in particular EDF, and report the findings as reports
• Support the work of academics, research associates and research students within the research group
• Identify new research activities and pursue them in close collaboration with the academic staff
• Write scientific papers for journal publications and conferences and attend national and international conferences and workshops

• Applicants should have a PhD in Mechanical Engineering, Materials Science, mathematics, physics or relevant field with emphasis on machine learning.
• You will have experience of relevant theoretical and numerical machine learning approaches and having expertise in structural behaviour modelling will be advantages.
• You will have developed and then delivered high-quality research projects in an academic and/or industrial context.
• You will have an excellent record of relevant publications in academic journals and conferences.

For informal queries please contact Professor Mahmoud Mostafavi: [email protected].

To find out more about what it's like to work in the Faculty of Engineering, and how the Faculty supports people to achieve their potential, please see our staff blog:

https://engineeringincludesme.blogs.bristol.ac.uk/

Interviews are anticipated to be held on 15 March 2024.

We recently launched our strategy  to 2030 tying together our mission, vision and values.

The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.

A senior postdoctoral position is available to deliver research activities on machine learning to develop surrogate models (emulators) of complex structural materials models. This role is being advertised as part of a five-year project (commenced in May 2021) called SINDRI . SINDRI’s aim is to deliver a step change in the design and assessment of structural materials deployed in nuclear power plants using easy to use yet accurate models. The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) and EDF.

This is an exciting opportunity for a postdoctoral researcher, with experience in applying machine learning techniques to physical models and simulation of solids. You will be collaborating with a wide range of academic and industrial partners (including but not limited to EDF, UKAEA, Rolls-Royce, Jacobs, and NNL) and work on projects with major national and industrial relevance.  It will allow them to participate in world-leading research in the Solid Mechanics Research Group (SMRG ) within the School of Electrical, Electronic and Mechanical Engineering (EEME) at the University of Bristol.

Other researchers in the project will develop multi-physics models to simulate the fabrication and in-service behaviour of representative alloy systems and validate them against high fidelity experiments such as synchrotron diffraction. Your roll will be to produce surrogate models of those physics-based models using techniques such as Gaussian Process, neural network, or polynomial chaos expansion to bridge the gap between different length-scales. The easy-to-use and fast emulators will be used by engineering practitioners at our industrial partners.

Research Responsibilities

• Deliver a portfolio of surrogate modelling
• Liaise closely with researchers at partners universities who carry out experiments and develop models
• Coordinate research activities and objectives with industrial partners, in particular EDF, and report the findings as reports
• Support the work of academics, research associates and research students within the research group
• Identify new research activities and pursue them in close collaboration with the academic staff
• Write scientific papers for journal publications and conferences and attend national and international conferences and workshops

• Applicants should have a PhD in Mechanical Engineering, Materials Science, mathematics, physics or relevant field with emphasis on machine learning.
• You will have experience of relevant theoretical and numerical machine learning approaches and having expertise in structural behaviour modelling will be advantages.
• You will have developed and then delivered high-quality research projects in an academic and/or industrial context.
• You will have an excellent record of relevant publications in academic journals and conferences.

For informal queries please contact Professor Mahmoud Mostafavi: [email protected].

To find out more about what it's like to work in the Faculty of Engineering, and how the Faculty supports people to achieve their potential, please see our staff blog:

https://engineeringincludesme.blogs.bristol.ac.uk/

Interviews are anticipated to be held on 15 March 2024.

We recently launched our strategy  to 2030 tying together our mission, vision and values.

The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.