Research Support Assistant (Machine Learning, Computer Vision)

The role We invite applications from data scientists, computer scientists, geographers and economists specialised in machine learning and computer vision. The position would be hosted by the School of Economics at the University of Bristol, where the candidate would benefit from an interdisciplinary, international research environment, and would be able to gain important research experience and prepare PhD applications. The successful candidate may also join the Jean Golding Institute for data science, and benefit from our institutional partnership with the Alan Turing Institute or connections with other departments at the University of Bristol (e.g., Geographical Sciences, Engineering or Computer Science). The research intends to develop novel, interdisciplinary methods that facilitate the systematic extraction of information from historical maps and other images (e.g., paintings) to study the evolution of cities and countries in the long run. The project is an international collaboration of economists with an interest in quantitative methods involving: Yanos Zylberberg (Bristol, PI), Laurent Gobillon (PSE-CNRS), Stephan Heblich (Toronto), Clement Gorin (Paris 1 Sorbonne), Pierre-Philippe Combes (ENS Lyon), Marcel Fortin (Toronto), Steve Redding (Princeton), Gilles Duranton (Wharton UPenn). The project is funded by Open Research Area (ORA) for the Social Sciences ANR-DFG-ESRC-SSHRC. What will you be doing? The research assistant would be expected to support the research project “MAPHIS: Mapping History--What Historical Maps Can Tell Us About Urban Development”, an international collaboration between researchers in the UK, France, Canada and the United States. You should apply if The ideal candidate will hold a MSc/MRes (or be an exceptional BSc student)—or be close to completion—in a relevant field (engineering, computer science, data science, economics, economic geography) and should have experience/knowledge in the following area: Developing machine-learning classification techniques for visual recognition. Additional information For informal queries, please contact Yanos Zylberberg, yanos.zylberberg@bristol.ac.uk  Our strategy and mission We recently launched our strategy to 2030 tying together our mission, vision and values. The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.