Knowledge Transfer Partnership Associate in Advanced and Digitalised Construction

Economics Network; Manchester; Hybrid; GBP 38,000 - 46,000; Full time

Benefits offered by this job

Pension

Annual leave

Ride to work/EV scheme

Job summary

University of Manchester invites applications for a Knowledge Transfer Partnership Associate in Advanced and Digitalised Construction. The 36-month project partners with Patented Housing ltd to optimise prefabrication processes using advanced manufacturing, aiming to enhance efficiency, safety and environmental sustainability.

The role involves developing simulation models, optimizing workflows, and disseminating findings through journals, reports and workshops, with collaborations across

Qualifications

• Candidates should hold a PhD (or equivalent) in a relevant field such as Mechanical Engineering, Aerospace Engineering, Electrical & Electronics Engineering, Computer Science, Digital Construction or a related discipline
• Demonstrable experience in modelling, simulation, and optimisation of industrial workflows and processes, especially in advanced manufacturing and/or prefabricated construction
• Proficiency in systems design and optimisation, programming, and practical implementation, with a focus on advanced manufacturing in construction
• Strong skills in simulation methodologies
• Excellent communication and interpersonal skills

Responsibilities

• Develop simulation models by creating and validating advanced simulation models to replicate dynamic prefabricated construction sites
• Enhance prefabricated construction processes by identifying areas of inefficiency, safety risks and design mitigation strategies, and align them with industry standards
• Optimise task allocation by designing strategies to streamline task design and allocation to improve safety, productivity, and environmental sustainability
• Collaborate with stakeholders by engaging with academics and industry partners to refine models, strategies, and project outputs
• Disseminate project findings by publishing results in academic journals, generate industry reports, and lead workshops

Education

PhD or equivalent

Tools

AI/ML

IoT devices

Digital twins

Sensors

Job description

Job reference: SAE-032319

Salary: £37,694 - £46,049 per annum depending on experience

Faculty/Organisational Unit: Science and Engineering

Location: Oxford Road

Employment type: Fixed Term

Division/Team: Department of Mechanical and Aerospace Engineering

Hours Per Week: Full time (1 FTE)

Closing date (DD/MM/YYYY): 30/09/2026

Contract Duration: Fixed term for 36 months

School/Directorate: School of Engineering

We are seeking a motivated and collaborative individual to join our team as a Knowledge Transfer Partnership Associate in Advanced and Digitalised Construction. This 36-month Knowledge Transfer Partnership project with the University of Manchester and Patented Housing ltd will use advanced manufacturing techniques to optimise and digitalise the prefabrication processes of low-cost housing. The role will focus on enhancing efficiency, environmental sustainability, and safety in prefabricated construction.

You will be responsible for:

• Develop simulation models by creating and validating advanced simulation models to replicate dynamic prefabricated construction sites
• Enhance prefabricated construction processes by identifying areas of inefficiency, safety risks and design mitigation strategies, and align them with industry standards
• Optimise task allocation by designing strategies to streamline task design and allocation to improve safety, productivity, and environmental sustainability
• Collaborate with stakeholders by engaging with academics and industry partners to refine models, strategies, and project outputs
• Disseminate project findings by publishing results in academic journals, generate industry reports, and lead workshops

We welcome candidates who bring diverse perspectives, experiences, and approaches to their work.

About you

We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate:

Essential criteria:

• Candidates should hold a PhD (or equivalent) in a relevant field such as Mechanical Engineering, Aerospace Engineering, Electrical & Electronics Engineering, Computer Science, Digital Construction or a related discipline
• Demonstrable experience in modelling, simulation, and optimisation of industrial workflows and processes, especially in advanced manufacturing and/or prefabricated construction
• Proficiency in systems design and optimisation, programming, and practical implementation, with a focus on advanced manufacturing in construction
• Strong skills in simulation methodologies
• Excellent communication and interpersonal skills

Desirable criteria:

• Familiarity with machine learning algorithms and artificial intelligence as applied to advanced manufacturing
• Understanding of organisational psychology principles related to advanced manufacturing and prefabricated construction
• Knowledge of construction workflows, equipment, and safety protocols
• Familiarity with IoT devices, digital twinning, and sensors for advanced manufacturing applications in construction
• Experience in mentoring or supervising workers, junior researchers, or students

We value transferable skills and real-world experience as much as formal qualifications.

Our benefits include:

• Generous employer contribution pension
• 29 days annual leave plus bank holidays, along with Christmas closure
• Ride to work and EV car scheme available

For more information, please see University of Manchester Benefits. You can also find information on our Flexible and Hybrid workinghere.

We are an open place of enquiry and challenge. We embrace and celebrate difference, diversity and debate, and we pride ourselves on being a place of education, learning and community where we are able, within the law, to question and test received wisdom, express new ideas and explore controversial or unpopular topics and opinions. Find out more from our Freedom of Speech Policy.

Enquiries about the role, shortlisting and interviews

Name: Dr. Akilu Yunusa-Kaltungo and Dr Clara Cheung

Email address:

General enquiries and administrative support
[e-mail]

Technical and job portal supportjobseekersupport.jobtrain.co.uk/support/home

Applications close at midnight on the closing date.

£37,694 to £46,049 per annum depending on experience