Research Fellow in Modelling and Optimisation

Role Description

About the Role

We are looking for an enthusiastic researcher to develop advanced modelling and optimisation methods to support the transition towards lower-carbon, more efficient and resilient transport systems and AI enabled circular manufacturing supply chains. Working with researchers, industry partners, and stakeholders, you will develop and apply computational and AI based approaches to analyse scenarios across complex transport and manufacturing systems and identify improved operational, technological, and investment strategies. The work will consider interactions between operations, infrastructure, energy demand, technology transition, cost, emissions and operational constraints. A major focus of the role will be developing modelling and optimisation approaches that support evidence-based decision-making. This may include optimising transport/manufacturing operations, alternative fuel/ technology pathways, infrastructure capacity, fleet/machine composition, resource allocation, scheduling, resilience strategies, and investment sequencing. The research will support questions such as how transport/manufacturing systems can reduce emissions while maintaining service performance, which technologies and infrastructure to prioritise, how to sequence investment pathways, and how to balance cost, carbon, resilience, and operational performance. You will work closely with colleagues developing simulation models, digital twins, data architectures and federated transport/manufacturing models. This role will provide the optimisation and decision-support capability required to analyse and improve the performance of complex systems. The role offers an opportunity to contribute to high-impact research addressing the UK’s major net-zero challenges while developing modelling and optimisation approaches with wider applicability across transport, energy, manufacturing and other complex engineering systems.

About You

You will have a Doctorate degree or equivalent (PhD/EngD), or be close to completion, in a relevant subject such as: Engineering; Operational Research; Applied Mathematics; Computer Science; Systems Engineering; Data Science; or a closely related discipline. You should have strong experience in optimisation, computational modelling, operational research or quantitative decision analysis, together with the ability to formulate complex real-world engineering or operational problems as tractable optimisation problems.

We are particularly interested in applicants with experience in one or more of the following: mathematical programming; multi-objective optimisation; simulation-based optimisation; stochastic and robust optimisation; heuristic and metaheuristic optimisation; transport, logistics, energy or infrastructure optimisation; resource allocation and scheduling; cost, carbon or sustainability optimisation; uncertainty and sensitivity analysis; and machine learning or AI-supported optimisation. Strong analytical and programming skills are essential. Relevant experience may include tools and languages such as Python, MATLAB, R, Java, and optimisation packages or solvers such as Gurobi, CPLEX, Pyomo or equivalent platforms. Experience with modelling or simulation environments such as AnyLogic, MATSim or Simulink would also be valuable. You should be able to work with complex datasets, translate stakeholder requirements into optimisation problems, evaluate alternative strategies, interpret results critically and communicate findings clearly to both technical and non-technical audiences. Knowledge of transport decarbonisation, energy systems, sustainable engineering or whole-life decision-making would be advantageous but is not essential where you can demonstrate effective transferable optimisation expertise. You will also have excellent communication, teamwork and time-management skills, together with the ability to manage your own research activities and contribute effectively within a multidisciplinary team. Experience of collaborative research involving academic and industrial partners, externally funded research programmes, and publication in high-quality peer-reviewed journals would be advantageous.

About Us

As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here.

Cranfield’s research and educational excellence is recognised internationally and is brought together in the Faculty of Engineering and Applied Sciences. The faculty hosts the associated core research and education ‘Themes’ which in turn host a number of world-class centres and institutes. The themes define their own research challenges and educational missions, with the Directors of Theme working closely with the Deputy Vice-Chancellor (FEAS).

Cranfield Manufacturing, Materials and Design community are recognised for the delivery of niche postgraduate research, education, and consultancy.  We are unique in our multi-disciplinary approach to innovation and problem solving for industry, bringing together design, materials’ technology, and manufacturing expertise to create smart, clean, and green solutions with Net Zero UK 2050 in mind.

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here.

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity Working Families. Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working here.

Working Arrangements

Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners.

How to apply                                   

For an informal discussion about this opportunity, please contact Dr Maryam Farsi, Senior Lecturer in Engineering Optimisation on (T):+44 (0)1234 750111 x 4647 or (E): maryam.farsi@cranfield.ac.uk

Please do not hesitate to contact us for further details on E: peoplerecruitment@cranfield.ac.uk. Please quote reference number 5395.

Closing date for receipt of applications: 12 October 2026

Please note that we reserve the right to close this advert prior to the stated closing date should we receive sufficient numbers of applications. Therefore, we would encourage you to complete and submit your application as soon as possible.