The job
Cities and governments worldwide are developing increasingly ambitious climate adaptation plans, yet we know little about whether these commitments translate into implemented action. We invite applications for a 2-year (extendable for 1 year) postdoctoral position to develop an evidence-based framework for assessing the transition from adaptation planning to implementation. Using computational methods such as natural language processing, machine learning, data mining, and quantitative analysis, the postdoctoral researcher will develop methods to track adaptation actions over time. The successful candidate will join the Computational Social Science for Extreme Events research group led by Prof. Mariana de Brito at the UFZ, working at the intersection of geography, climate research, and computational social science. The position offers substantial scope to develop an independent research profile while contributing to establishment of the Transformation Observatory in the Research Unit Environment and Society of the UFZ .
Place of work
Leipzig
Working time
100% (39h/week)
The job position is available for full-time or part-time employment.
Contract limitations
limited contract / 24 months
Salary
Remuneration according to the TVöD public-sector up to pay grade 13 including attractive public-sector social security benefits.
Contact
Your contact for any questions you may have about the job:
Mariana Madruga de Brito
mariana.brito[at]ufz.de
Your application
To ensure a fair selection process, please submit your application (cover letter, CV, and relevant supporting documents) via our online portal without a photo, age information, or details about your marital status.
Diversity and Inclusion
The UFZ values diversity and is actively committed to ensuring equal opportunities for all employees, regardless of their origin, religion, beliefs, disability, age or sexual identity.
We welcome people who represent diverse backgrounds, identities and perspectives. We therefore particularly encourage people who are affected by structural discrimination to apply to us.
The UFZ
The Helmholtz Centre for Environmental Research (UFZ) is a world-leading institution in environmental research and a member of the Helmholtz Association, Germany’s largest scientific organisation. With approximately 1,200 employees across Leipzig, Halle, and Magdeburg, we have been conducting research since 1991 using a transdisciplinary approach to address the most pressing challenges of our time: biodiversity loss, climate change, and environmental pollution. Our goal is to translate excellent research into practical solutions for policymakers, business, and society, and to serve as a reliable partner in supporting transformation processes toward a sustainable and just future for current and future generations. We foster a culture of collaboration, openness, and diversity within a work environment that actively promotes creativity and personal development.
Your tasks
- Develop and apply natural language processing (NLP) methods and/or large language models (LLMs) to identify and extract evidence on adaptation planning and implementation from text data
- Link extracted information with independent sources of evidence on implementation, including city monitoring reports, public procurement records, and other administrative and policy datasets (e.g., Covenant of Mayors monitoring reports and the EU’s Tenders Electronic Daily database)
- Develop indicators for measuring adaptation implementation and progress
- Investigate how implementation relates to climatic, socioeconomic, institutional, and urban characteristics
- Disseminate results through peer-reviewed publications, conference presentations, and interdisciplinary collaboration
- Contribute to the development of the research group and, where appropriate, co-supervise students
We offer
- The freedom to master even the most demanding challenges between basic research and practical application
- The chance to work in interdisciplinary, international teams and benefit from a variety of perspectives
- Firstclass integration into national and international research networks to work together on global challenges
- Excellent research infrastructure and research data management to optimally support your work
- Flexible working hours and a wide range of options for balancing work and care responsibilities through our family office
- Competent support and advice for international colleagues arriving at the UFZ from the 'International Office'
- Special annual payment, capital-forming benefits and subsidised Germany Job Ticket
- A workplace in a vibrant region with a high life quality and social and cultural diversity
Your profile
- PhD degree in computational social science, data science, digital humanities, geography, environmental sciences, computerscience, urban studies, quantitative sociology, or a related field
- Good command of scientific programming (e.g., Python or R)
- Experience with one or more of the following: natural language processing, machine learning, text mining, large language models, data mining, or statistical analysis
- Good command of written and spoken English
- Strong scientific writing and communication skills
- Experience working with large and/or heterogeneous datasets is an advantage
- Interest in topics related to climate change adaptation, disaster risk reduction and environmental policy
We are looking for a candidate (f/m/x) with a strong quantitative profile, ideally combined with experience in natural language processing (NLP) and text-as-data methods. You should be comfortable working with large, messy, real-world datasets and have interest in linking and integrating heterogeneous data sources: from policy documents to socio-economic data.
We are looking for a postdoctoral researcher (f/m/x) to investigate whether and how urban climate adaptation plans translate into real-world action. Using large-scale policy, monitoring reports, and other administrative data, the project will use computational methods to develop new ways of measuring adaptation implementation.
To ensure a fair selection process, please submit your application including:
- a short cover letter,
- a CV including grades, links to your 2 most relevant publications, and the names and contact of 2 referees and
- relevant supporting documents such as certificates and transcripts.
Applications without the requested documentation will not be considered.
Application deadline: 30.11.2026
