KTP Associate

ABOUT THE ROLE

Working as part of a multidisciplinary team, you will support the Principal Investigator in delivering an exciting Knowledge Transfer Partnership (KTP) with Frameclad Ltd, focused on developing an innovative digital design and optimisation platform for fire-safe Light Steel Frame (LSF) wall and floor systems. As the KTP Associate, you will lead the research, design, development, validation and implementation of the project, applying cutting-edge research in structural fire engineering, computational modelling, digital engineering and structural optimisation to embed new knowledge, advanced digital technologies and innovative engineering solutions within the business. 

Working closely with the academic team at Northumbria University and engineers at Frameclad Ltd, you will design, conduct and analyse advanced fire experiments on LSF wall and floor systems, develop thermo-mechanical numerical models using Finite Element Analysis (FEA), undertake Computational Fluid Dynamics (CFD) modelling to simulate realistic fire scenarios, and carry out structural optimisation and parametric studies to improve the fire performance and efficiency of LSF systems. You will also contribute to the development of the Frameclad Product Design Toolkit (Frameclad-PDT) by integrating advanced computational models and digital engineering approaches into practical design tools for industry. In addition, you will be responsible for managing the day-to-day technical delivery of the KTP project, ensuring project milestones and Innovate UK reporting requirements are achieved, while presenting technical findings and project progress to Frameclad Ltd management, Northumbria University academics, Innovate UK representatives and the KTP Local Management Committee. This role offers an excellent opportunity to work at the interface between academia and industry, delivering innovative research with real-world impact while developing expertise in structural fire engineering, computational modelling and digital engineering.

 

This is a fixed-term post for the duration of the KTP project (2 Years)

ABOUT THE TEAM

This Knowledge Transfer Partnership (KTP) project is being delivered within the School of Civil Engineering, part of the Faculty of Science and Environment at Northumbria University, in collaboration with Frameclad Ltd, a leading UK manufacturer of Light Steel Frame (LSF) building systems. The successful candidate will be supervised by A/Prof (Dr) Keerthan Poologanathan and will work closely with a multidisciplinary team of internationally recognised academics and experienced industry engineers at the forefront of structural fire engineering, computational modelling, digital engineering and modern methods of construction. This collaborative project provides a unique opportunity to undertake high-impact applied research, develop innovative digital engineering solutions, and work at the interface between academia and industry to deliver research with significant commercial and societal impact.

ABOUT YOU

Applicants should hold a First-Class or Upper Second-Class BEng, MEng or MSc in Civil Engineering, Structural Engineering, Mechanical Engineering, or a closely related discipline. A PhD in Structural Engineering, Fire Engineering, Civil Engineering, Mechanical Engineering, or a related discipline is highly desirable. Candidates should have demonstrable specialist knowledge and experience in structural fire engineering, structural behaviour in fire, fire safety engineering, together with expertise in Finite Element Analysis (FEA), thermo-mechanical numerical modelling, and Computational Fluid Dynamics (CFD). Experience in the design, analysis and modelling of structural systems under fire conditions, supported by strong analytical, computational and problem-solving skills, would be highly advantageous.

Experience or knowledge in any of the following areas would also be highly desirable: cold-formed steel structures, Light Steel Frame (LSF) construction, Eurocodes, Modern Methods of Construction (MMC), structural optimisation, Design for Manufacture and Assembly (DfMA), machine learning, artificial intelligence (AI), digital engineering, parametric modelling, and programming using Python, MATLAB or similar engineering software. The successful candidate should possess excellent communication, organisational and project management skills, with the ability to work effectively within multidisciplinary academic and industrial teams, manage multiple priorities, and contribute to the successful delivery of an innovative research project with significant industrial impact.

Further information about the requirements of the role is available in the person specification.  

If you would like an informal discussion about the role, please contact [email protected].

To apply for this vacancy please click 'Apply Now'.  Your application should include a covering letter and a CV.