Research Fellow in Mobility Data Science

4 weeks ago

Leeds, United Kingdom University of Leeds Full time

**Research Fellow in Mobility Data Science**:
**Are you an ambitious researcher interested in human mobility behaviour? Are interested in pushing forward the state-of-the-art in mobility data science, through large-scale mobility data? Do you want to further your career in one of the UK's leading research-intensive Universities?**

We are delighted to be recruiting two new two-year Research Fellows in Mobility Data Science to work on the cutting edge of human mobility analysis. The Research Fellows will use large-scale mobility data, in combination with machine learning and statistical measures, to answer critical questions about the nature of mobility behaviour and its role within wider social and spatial systems.

The Research Fellows we recruit will work closely with each other, and join an active, ambitious, and interdisciplinary research group of other Fellows and PhD students, all working on mobility challenges at different scales and contexts. Our recent research has explored how mobility data and models can derive new insights into health, sustainability, human cognition, inequalities, and economic activity. While there are commonalities in the requirements and approaches required of the two posts, they relate to and will have responsibility for one of two distinct projects.

**Post 1 (Indoor Movements and Interactions)**

Working with mobility data collected within a large hospital environment, to understand patterns of movement, activity, and interaction. The research will make advances to the state-of-the-art in trajectory analysis, drawing in methods from machine learning, and developing novel approaches to deal with uncertainty, privacy protection, production of synthetic data, and ensuring data reliability. The postholder will have opportunities to work with data from a variety of sources, including Bluetooth sensors and routinely collected activity records. There will also be opportunities to develop linkages between mobility and health-related data (e.g. infections, virology), opening opportunities to identify patterns of infection transmission over precise space and time. The research is funded through the EPSRC i-sense and Digital Health Hub projects, and will involve close collaboration with partners at University College London, University College Hospital, and University of Sheffield.

**Post 2 (Urban Navigation)**

The post will focus on the development of novel predictive models of urban spatial navigation. The project will make use of large-scale real-world trajectory data in combination with machine learning and the theory of spatial cognition, to derive theoretically and scientifically robust models of movement. The project is expected to make high-impact contributions in mobility behavioural research. As such, it is expected that the Research Fellow will be comfortable with concepts from the broad literature around mobility and/or navigation behaviour, and be confident in translating these ideas into computational models. This research is funded by the Leverhulme Trust and the Alan Turing Institute, with whom there will be regular interaction.

Both posts will be based in Professor Ed Manley's research group, located between the School of Geography and Leeds Institute for Data Analytics (LIDA). As members of an active research group, based at a major research-intensive University, the post holders will have ample opportunities to develop their research skills, build interdisciplinary collaborations, and contribute world-leading mobility research.

**To explore the post further or for any queries you may have, please contact**:
Professor Ed Manley, Professor of Urban Analytics and Pro-Dean for Research and Innovation

**Location**:

- Leeds - Main Campus**Faculty/Service**:

- Faculty of Environment**School/Institute**:

- School of Geography**Category**:

- Research**Grade**:

- Grade 7**Salary**:

- £37,099 to £44,263 p.a.**Working Time**:

- 100% - We will consider job share / flexible working arrangements**Post Type**:

- Full Time**Contract Type**:

- Fixed Term (24 months)**Release Date**:

- Wednesday 08 November 2023**Closing Date**:

- Wednesday 06 December 2023**Reference**:

- ENVGE1220

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