Research Fellow in Choice Modelling and Machine Learning
Do you have research expertise in Machine Learning and/or Choice Modelling? Are you interested in conducting methodological research to bridge these disciplines? Would you like to implement novel methodologies to advance the state-of-the-art transport models and make a real-world impact?
Transport choice models have historically relied on manually collected survey data that are expensive to obtain and generally have limited sample sizes and lower update frequencies. They are also prone to biases and reporting errors. On the other hand, over the last decade, passively collected big data sources have emerged as a very promising source of mobility data for researchers and practitioners. These include GPS traces, mobile phone records, bank and loyalty card transactions and geo-coded social-media data. However, the application of these data have been primarily limited to visualizations and development of machine learning based predictions.
The machine learning techniques for analyzing big data are however very often data-driven and lack behavioural underpinning which questions there applicability in predicting behaviour in radically different future scenarios. The effective combination of machine learning and choice modelling offers the promise to make better use of the strengths of both types of data and enable us to develop better and more comprehensive models, potentially at lower costs.
The NEXUS project, funded by the Future Leader Fellowship Programme of the UKRI, looks at developing next-generation mathematical models of travel behaviour that can better predict the decisions made by travellers in the fast-evolving mobility landscape by augmenting traditional choice modelling techniques with insights from machine learning. We are looking for a committed, highly motivated and innovative individual with machine learning and/or choice modelling background to work in this exciting long-term research project.
As a member of the team, you will be working with Dr Charisma Choudhury (UKRI Future Leader Fellow) and Dr He Wang (Co-Investigator, School of Computing). You will be based at Institute for Transport Studies (ITS) with cross-university collaborations through the Choice Modelling Centre (CMC) and Leeds Institute for Data Analytics (LIDA). You are expected to contribute to methodological research on bridging machine learning and choice modelling in the context of transport. You will also contribute to the empirical component of the project and have the opportunity to work with researchers at the Alan Turing Institute – UK’s national centre for data science and artificial intelligence as well as two non-academic partners: Citi Logik and Asian Development Bank. As this is a multi-faceted research project, you will be able to contribute to individual components of the work as well as helping to shape the direction of the research according to your own interests and background. You will be expected to take academic ownership of large parts of the programme and make a lasting contribution to the field.
To explore the post further or for any queries you may have, please contact:
Charisma Choudhury, Associate Professor
Tel: +44 (0)113 343 2659, email: email@example.com
Location: Leeds - Main Campus
Faculty/Service: Faculty of Environment
School/Institute: Institute for Transport Studies
Grade: Grade 7
Salary: £33,797 to £40,322 p.a. Due to funding constraints, an appointment will not be made above £36,914 p.a.
Working Time: 100% - We will consider flexible working arrangements
Post Type: Full Time
Contract Type: Fixed Term (until 14 November 2022)
Closing Date: Wednesday 02 September 2020
Downloads: Candidate Brief