NERC DREAM-CDT funded PhD position in urban resilience for shrinking cities and big data

University of Groningen
Birmingham
7 months ago
Create job alert
  • NERC DREAM-CDT funded PhD position in urban resilience for shrinking cities and big data

Tuesday, 07 August 2018 09:06NERC DREAM-CDT funded PhD position in urban resilience for shrinking cities and big data

Topic: Building resilient cities in the shrinking paradigm: A UK and China comparison using new sources of big data

Funding eligibility: Funding is available for UK residents (fees + stipend) and EU residents (fees only).

Application closing date: August 15, 2018

Project description: Urban shrinkage is a common phenomenon throughout the world despite urbanisation being a well-established trend. With increasing globalisation, cities in both developed and developing counties experience economic downturn, population decline, de-urbanisation. Reasons and solutions of urban shrinkage have been discussed and documented extensively for developed countries (e.g. UK, US, Germany, and Japan). However, deeper understanding of urban shrinkage issues and how to resolve them in the developing world, especially in China with a large number of fast growing cities, is still lacking. Insights from developed countries could be learned in order to better address the challenges for building resilience into shrinking cities of the developing world. Northeast China provinces, including Liaoning, Jilin, and Heilongjiang, now known as the “rust belt” in China, have topped the chart in the number of shrinking cities due to resource depletion, deindustrialization, and demographic changes. Similarly, most of the top UK declining cities are in the north of England as a strong indication of the North-South divide. Core cities of North England, such as Liverpool, Manchester, Leeds, Sheffield and Newcastle, share some common characteristics with their counterpart in Northeast China in terms of industrial legacy, aging population, and loss of growth power to support surrounding areas. Insights could be gained for those cities in both countries by a comparative study of their resilience to internal and external changes.

With a focus on Northeast China cities, this project seek to 1) identify and better understand the spatial, economic and social issues of shrinking cities and the underpinning mechanisms in relation to other Chinese cities, and 2) design adaptive strategies to build resilience into these cities through a comparative study of urban shrinkage in China and UK. This project will expand the existing research by combining the spatial, economic and social dimensions of human mobility and urban interactions and considering the interplay of all three dimensions in defining a multidimensional measurement and assessment of urban resilience. Furthermore, this project will promote the collaboration between the UoB research team and the Chinese stakeholders in order to incorporate local interests and benefit decision-makers with both general and place-based strategies in policy-making.

This project will facilitate the identification and acquisition of various traditional and novel sources of data, which can be leveraged them to gain better insights by leading-edge big data analytics and AI techniques. The substantive and methodological knowledge that this PhD project will generate will directly contribute to the UK Industrial Strategy Grant Challenge of Artificial Economy and the Data Economy as well as on the Key Policies on Infrastructure and Places. Moreover, this PhD project will contribute to the research objectives of the Alan Turing Institute, which the University of Birmingham recently joined. The latter signifies the broader recognition of AI and the Data Economy as a research priority for the University of Birmingham.

Qualification: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Geoinformatics, GIScience/Geocompuation, Transport Planning, Civil Engineering, Geography, Environmental Science, Computer/Data Science or Urban Planning. Applicants with skills in quantitative modelling or Python/R programming are preferred.

For further details: Please contact Dr Zhaoya Gong (Birmingham):

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.

Get In Touch

Regional Science Association International


#J-18808-Ljbffr

Related Jobs

View all jobs

Job announcement: Lecturers/Senior Lecturers in Human Geography (Big Data)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.