英國(guó)利茲大學(xué)2023年招聘博士后職位（選擇建模，機(jī)器學(xué)習(xí)和數(shù)學(xué)心理學(xué)，3個(gè)職位）

利茲大學(xué)（University of Leeds），是一所位于英國(guó)利茲的公立綜合性研究型大學(xué)、世界百?gòu)?qiáng)名校，英國(guó)紅磚大學(xué)，是羅素大學(xué)集團(tuán)，世界大學(xué)聯(lián)盟、國(guó)際大學(xué)氣候聯(lián)盟、歐洲大學(xué)協(xié)會(huì)、英聯(lián)邦大學(xué)協(xié)會(huì)、中英大學(xué)工程教育與研究聯(lián)盟、RENKEI、N8大學(xué)聯(lián)盟、白玫瑰大學(xué)聯(lián)盟成員。利茲大學(xué)商學(xué)院獲AACSB、EQUIS和AMBA三重認(rèn)證，是全球商學(xué)院網(wǎng)絡(luò)、“一帶一路”商學(xué)院聯(lián)盟、中歐商校聯(lián)盟成員。

Research Fellow in Choice Modelling, Machine Learning and Mathematical Psychology (3 posts)

University of Leeds

Job Description

Do you have research expertise in Choice Modelling, Machine Learning and/or Mathematical Psychology? 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 in behavioural modelling and make a real-world impact?

Understanding the mechanisms and the drivers behind people’s choices has been the focal point of a range of academic disciplines and a key component in policy making, for example aiming to shift behaviours for a more sustainable future. Mathematical process models from psychology, behavioural econometric models and data-driven machine learning algorithms have been developed in parallel with only limited knowledge spillovers among them, but with the same core goal of understanding individual decision making.

Models from Mathematical Psychology focus on understanding the processes leading to decisions. These are often described as causal models aiming to answer how an individual reached a certain choice and understand the interrelated processes behind that. Those models, however, are rarely applied outside a controlled lab setting, with only a handful of real-world applications.

Algorithms and techniques from Machine Learning, initially originating from Computer Science, are steadily gaining ground in almost every discipline due to their ability to efficiently find patterns in complex data. These methods are mostly concerned with what the outcome is and seek to closely replicate the patterns that led to the observed choices. Nonetheless, the general lack of interpretability in their outputs hinder their more widespread adoption for policy making, where a more robust statistical association between the target and explanatory variables is required.

Finally, Choice Modelling is a field of econometrics that sits between the two aforementioned disciplines aiming to answer which factors influence the observed choices and to what extent, as well as what it would take to shift to a different choice. Choice models have been used extensively by policy makers and industry since the early theoretical advancements of 1970s. Despite their widespread use, however, these models are not able to provide clear links between the observed choices and the actual causal processes behind them as do models from Mathematical Psychology. Furthermore, they are arguably increasingly limited in their application to more complex data compared to Machine Learning algorithms, a theme that will continue to exacerbate with the advent of Big Data.

With this in mind, the synergy of those three disciplines and the development of new state-of-the-art modelling frameworks holds the promise of providing new Data-Driven Behavioural Models (DDBMs) combining the strengths and addressing the distinct limitations of the three areas. The development of DDBMs will come at a time when big data sources constantly challenge traditional modelling approaches. Additionally, the growth in human-machine interactions, such as with the advent of autonomous vehicles, will require the development of AI consistent with human behaviour to guarantee public safety and wider adoption in the market.

The Choice Modelling Centre at the Institute for Transport Studies seeks to hire an early career researcher to take part in the ERC-funded project “SYNERGY”. The project’s aim is to combine key techniques from Mathematical Psychology, Choice Modelling and Machine Learning, and help to develop new approaches. The role will involve working at the cutting edge of the three aforementioned areas of research for developing modelling frameworks that will actively shape future policy making. The successful candidate will need to demonstrate sufficient theoretical and technical knowledge of at least one of the three key disciplines involved in the project and possess an open mind to new ideas and approaches. Knowledge of a programming language, with an emphasis on R and/or Python, is also strongly advised.

As a member of the team, you will be based at the Institute for Transport Studies (ITS) where you will work with Professor Stephane Hess and other researchers in the Choice Modelling Centre (CMC), drawing also on expertise in its global network. You will become part of a highly productive team, have the opportunity to work with the technically advanced driving and pedestrian simulators of the University of Leeds and take part in international conferences for the purpose of disseminating the findings of the research. You are expected to contribute to methodological research on bridging choice modelling, mathematical psychology and machine learning in the context of transport, health and environment. 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:

Stephane Hess, Professor of Choice Modelling, Director of the Choice Modelling Centre

Email: s.hess@leeds.ac.uk

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