Machine learning for sustainable transportation systems

This is an interdisciplinary position at the intersection of artificial intelligence, deep learning, and transportation systems.

The research student will work on research projects focused on developing innovative machine-learning solutions for optimizing the integrated forecast models of electric vehicles for future smart cities.

This will include socioeconomic and other multiple factors such as modelling vehicle choices and placement of charging stations along with reinforcement learning for optimizing the choice of system parameters.

The project will also aim to forecast future electric vehicles' energy efficiency and performance assessment over the years by the combination of physical and deep learning models.

Qualifications

A successful candidate also has:

• An excellent MSc degree in engineering, computer science, or a related field
• Experience with applying data analytics and machine learning methods
• Experience with deep learning algorithms, statistics, and learning theory
• Strong programming experience in Python or similar language
• Experience with physics-informed learning, graph neural networks (GNN), General regression neural network (GRNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) or physics-constrained generative neural network
• Proven research-based publications in deep learning are a plus.
• Excellent problem-solving skills.

References

• Deep reinforcement learning in transportation research: A review

• Machine learning for next-generation intelligent transportation systems: A survey

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Muhammad Shafique - Dr. Muhammad Shafique is currently a Lecturer (Assistant Professor) in the Department of Civil & Environmental Engineering, College of Engineering, Design and Physical Sciences at Brunel University London, Since May 2022. Prior to this, he was a Postdoc fellow in the Department of Architecture and Civil Engineering at the City University of Hong Kong. He received his Ph.D. degree in Architecture and Civil Engineering from the City University of Hong Kong and M.S in Smart City and Construction Engineering from the University of Science and Technology, Korea. Dr. Shafique’s research uses life cycle assessment, circular economy, and scenario modeling to identify the environmental problems of emerging products and systems. Historically, our society has taken a reactionary approach to the environment. By proactively understanding the environmental issues of technologies that are still under development, we can identify a greater number of optimal options and innovative solutions to avoid or reduce negative consequences. Shafique works on interdisciplinary topics including sustainable construction and transportation systems, green buildings, building information modeling (BIM), carbon-neutrality, sustainable design of energy systems, advanced fuels, sustainable and smart materials, machine learning, energy, environment & economics nexus, and climate change adaptation. One of his core areas of work includes developing an integrated modeling approach based on life cycle assessment and circular economy for sustainable construction and urban mobility. Looking forward, Dr. Shafique is determined to fight against climate change and reduce the carbon footprint of the civil and transportation industry as the key considerations while enabling a high level of sustainability and well-being for users. Shafique is always looking for talented and motivated Ph.D. students as well as new collaborators for research projects. To date, Dr. Shafique has published more than 40 papers in high-impact journals. In addition, Dr. Shafique is a peer reviewer for over 30 high-impact factor journals. Dr. Shafique is the Associate Editor of Frontiers in Energy Research; Frontiers in Sustainability and an Editorial Board Member of the Land, Buildings and Smart Cities (MDPI) journal. Shafique is at the top 2% most highly cited scientists for 2021and 2022 according to the Elsevier/Stanford list.