Research Fellow in Modelling and Optimisation

About the Role

Research Activities

• Optimise transport and manufacturing operations, alternative fuel and technology pathways, infrastructure capacity, fleet and machine composition, resource allocation, scheduling, resilience strategies, and investment sequencing.
• Work with colleagues developing simulation models, digital twins, data architectures, and federated transport and manufacturing models.
• Analyse how systems can reduce emissions while maintaining service performance and balance cost, carbon, resilience, and operational performance.

About You

• Hold or be close to completing a PhD or EngD in engineering, operational research, applied mathematics, computer science, systems engineering, data science, or a related subject.
• Have strong experience in optimisation, computational modelling, operational research, or quantitative decision analysis.
• Be able to formulate complex real-world engineering or operational problems as tractable optimisation problems.
• Have strong analytical and programming skills and the ability to work with complex datasets.

Relevant Experience

• Relevant methods may include mathematical programming, multi-objective, simulation-based, stochastic, robust, heuristic, or metaheuristic optimisation.
• Relevant applications may include transport, logistics, energy, infrastructure, resource allocation, scheduling, cost, carbon, or sustainability optimisation.
• Relevant tools may include Python, MATLAB, R, Java, Gurobi, CPLEX, Pyomo, AnyLogic, MATSim, or Simulink.
• Experience with transport decarbonisation, energy systems, sustainable engineering, or whole-life decision-making is advantageous but not essential.

Working Style and Collaboration

• Translate stakeholder requirements into optimisation problems, evaluate strategies, interpret results critically, and communicate findings clearly to technical and non-technical audiences.
• Have excellent communication, teamwork, and time-management skills and manage research activities effectively within a multidisciplinary team.
• Collaborative research with academic and industrial partners, externally funded programmes, and high-quality peer-reviewed publication is advantageous.

About Cranfield University

Working Arrangements

• The university provides opportunities for staff to combine on-site and remote working where the role allows, balancing the needs of staff, students, clients, and partners.