Post doc scholarship in Economic Geography and Machine Learning at Lund University

SCHOLARSHIP

In collaboration with Handelsbanken's Tore Browaldhs Stiftelse, the Department of Human Geography, Lund University announces a three-year postdoc scholarship. 

Position Details
The post-doc position is funded initially for 3 years, with the potential for extension by an additional 3 years.

Terms of scholarship

• Stipend: SEK 490,000/year, disbursed biannually in advance. The stipend allows for up to 20% additional employment, preferably teaching-related.
• Additional yearly funds: SEK 100,000 for mandatory health, accident, pension, and survivor insurance.
• Expenses support: SEK 50,000/year for research-related travel, literature, and equipment.

Research Environment
The Browaldh stipend-funded post-doc will be placed at the Department of Human Geography (KEG) at Lund University, benefiting from dynamic collaborations with mathematics, computer science, and CIRCLE (Centre for Innovation Research). This interdisciplinary affiliation offers unique opportunities to engage in innovative research, teaching, and professional development.

KEG – Department of Human Geography
KEG is one of Sweden's leading geography departments, featuring extensive international networks and methodological diversity. The post doc will integrate into a vibrant group of economic, urban, and rural geographers and engage with senior scholars specializing in economic geography and innovation studies. KEG increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and sustainable development.

Collaborations with Mathematics and Computer Science
The post-doc will also affiliate with Lund's Centre for Mathematical Sciences, renowned for research in machine learning, mathematics, and data science. Collaborative projects merge traditional geographic research with advanced computational methods such as graph neural networks (GNNs) and large language models (LLMs) to explore economic systems, labor markets, career pathways, and socio-environmental interactions. The post doc will actively contribute to an experimental spatial research lab funded by various national research foundations.

Professional Development
The post-doc will benefit from teaching opportunities, seminar series, and methodological workshops offered by KEG and the Faculty of Social Sciences, strengthening both academic expertise and interdisciplinary networks.

Required Qualifications

• Strong quantitative methods skills, ideally in computer science or a related field.
• Excellent communication and interest in interdisciplinary collaboration.
• Demonstrated programming skills, preferably in Python (e.g., PyTorch, NumPy, pandas, polars).
• Experience with spatial and/or temporal data analysis (geographic data, satellite imagery, register data).
• Familiarity with machine learning/deep learning techniques (e.g., GNNs, LLMs).
• Ability to manage large, heterogeneous datasets.
• Ability and willingness to collaborate in interdisciplinary teams.

Preferred Qualifications

• Experience in economic geography or geography of innovation.
• Background addressing social-science research questions.
• Familiarity with socio-economic datasets (e.g., register, patent, or input-output data).

APPLICATION INFORMATION

The application shall be written in English and shall include the following:

• Cover sheet with contact information
• Curriculum Vitae, with publication list, and names and contact details of at least 2-3 personal referees (maximum two pages)
• Copies of original degree certificates
• Selected publications (maximum of five) and a full list of publications.
• Description of the applicant’s expertise at the intersection of Economic Geography and Machine Learning

Last application date: August 31st

Starting date. As soon as possible

Send your application to: [email protected]

If you have questions regarding the scholarship position, please contact

Ola hall, [email protected]