Research Fellow, School of Engineering

Organisation/Company LIVERPOOL JOHN MOORES UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Final date to receive applications 24 Nov 2026 - 00:00 (UTC) Country United Kingdom Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

Liverpool John Moores University (LJMU) is a distinctive, unique institution, rooted in the Liverpool City Region and with a global presence. Our students and staff, past, present, and future, are the beating heart of our city and can be found in every corner of every industry and community. We couldn't exist anywhere else and have shaped the city in which we belong.

Working with the people of Liverpool to improve lives and support communities is at the heart of why we were founded and why we exist today.

We are now looking for highly motivated Research Fellow (PDRA) to develop and conduct individual and/or collaborative research projects. This is a 24-month fixed-term appointment supported by the EPSRC-funded Maritime and Last Mile Net Zero (Ma La Mi ) Place Based Impact Acceleration Account, under the supervision of Professor Zaili Yang, offering an opportunity to contribute to innovative research and practical solutions for maritime and last-mile decarbonisation.

You will support the development and application of quantitative and data-driven approaches to address challenges in maritime transportation, sustainable logistics and decarbonisation, with particular focus on Engineering Net Zero, Artificial Intelligence (AI), digitalisation and data. The work will contribute to evidence-based decision-making, the development of practical solutions and the advancement of sustainable transport systems.

• contributing to scoping, feasibility assessment and proof-of-concept development for maritime and last-mile decarbonisation projects
• supporting the development and application of quantitative and data-driven models, including modelling, simulation, optimisation and machine learning
• assisting in the development, testing and evaluation of analytical tools, prototypes and demonstrators, working collaboratively with academic researchers and external partners

You should possess a good honours degree and a PhD (or equivalent professional experience) in transport, logistics, engineering, operations research, data science or a relevant discipline. You must demonstrate strong quantitative research and data-analysis capabilities, proficiency in programming or analytical tools, and the ability to disseminate research findings through high-quality publications in international journals and refereed conferences. Experience with research or innovation projects related to maritime decarbonisation, sustainable logistics, AI, digitalisation or proof-of-concept development is preferred.

You will work as part of the EPSRC-funded Ma La Mi  programme, collaborating with an interdisciplinary team of researchers, investigators and external partners, within the Liverpool Logistics Offshore and Marine Research Institute (LOOM) at Liverpool John Moores University, UK. LOOM enjoys an international research reputation in maritime risk studies,  .

• generous annual leave entitlement
• pension scheme
• induction
• development support
• family-friendly policies

This is an exciting time to join the university as we deliver the LJMU Strategy 2030 and its vision of LJMU as an inclusive civic university transforming lives and futures, by placing students at the heart of everything we do.

The School/department is committed to promoting equality and diversity, including the Athena SWAN Gender Equality charter for promoting the career of women in STEM (science, technology, engineering, mathematics) in higher education. We particularly welcome applications from women for this post and all appointments will be made on merit.

Please note all of our vacancies will beclosed to applications at midnight on the advertised closing date, unless otherwise stated.