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Fully funded PhD Opportunity on 'Web archives and cities: mining the web to learn our cities'

University of Groningen

United Kingdom

Remote

GBP 10,000 - 40,000

Full time

30+ days ago

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An exciting opportunity awaits for a PhD candidate to delve into the intersection of urban studies and technology. This fully funded position will focus on mining web archives to uncover insights about the evolution of cities in the UK from 1996 to 2013. Ideal candidates will possess a strong background in social sciences or computer science, coupled with robust computational skills in R or Python. This innovative research project promises to contribute significantly to our understanding of urban dynamics, providing a unique platform for academic growth and exploration. Join a forward-thinking academic environment where your research can make a meaningful impact!

Relevant background in social sciences or computer science with an interest in urban studies.
Strong computational skills in R or Python are essential.

Explore city dynamics through mining online content from archived web pages.
Utilize unstructured data to understand urban changes in the UK.

Python

Natural Language Processing

Machine Learning

Statistical Knowledge

Social Science Background

Computer Science Background

Fully funded PhD Opportunity on 'Web archives and cities: mining the web to learn our cities'

Fully funded PhD Opportunity on 'Web archives and cities: mining the web to learn our cities'

The project will explore how the dynamics of cities are reflected in and, therefore, can be sensed by mining online content. It will utilise an innovative data source of billions of archived web pages under the .uk domain during the period 1996-2013. It will exploit the unstructured textual data contained in these webpages in order to understand the changes that cities in the UK have undergone.

We are looking for a student with:

- Relevant social science background in either geography/planning/urban studies or linguistics. Alternatively, a computer science background and willingness to engage with the above disciplines.

- Strong computational background including experience in R or Python.

- Good statistical knowledge.

- Preferably, experience in Natural Language Processing and Machine Learning.

The Regional Science Association International (RSAI), founded in 1954, is an international community of scholars interested in the regional impacts of national or global processes of economic and social change.

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Regional Science Association International

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