Knowledge Transfer Partnership Associate in Advanced and Digitalised Construction

The University Of Manchester; Greater Manchester; On-site; GBP 36,000 - 48,000; Full time

Benefits offered by this job

Generous employer contribution pension

29 days annual leave plus holidays

Job summary

The University of Manchester is seeking a Knowledge Transfer Partnership Associate for a 36-month project in Advanced and Digitalised Construction. You will develop and validate simulation models to optimise prefabricated construction processes, improve safety, productivity and environmental outcomes, and collaborate with academics and industry partners to publish findings and deliver workshops.

Applicants should hold a PhD in a relevant field and demonstrate expertise in modelling, simulation

Qualifications

• Candidates should hold a PhD 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 inefficiencies, safety risks and design mitigation strategies, aligning with industry standards.
• Optimise task allocation by designing strategies to streamline task design and allocation to improve safety, productivity, and environmental sustainability.
• Collaborate 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.

Skills

Modelling & Simulation

Industrial workflow optimisation

Systems design

Programming

Communication

Education

PhD in Mechanical/Aerospace/ENG/CS/Digital Construction

Tools

Python

MATLAB

AnyLogic

Job description

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 working here.

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 support

https://jobseekersupport.jobtrain.co.uk/support/home

Applications close at midnight on the closing date.

Further particulars (with person specification) linked below.