The role combines hands-on evidence synthesis with the development and evaluation of AI-driven approaches to make evidence synthesis processes more efficient. You will work on manual evidence-synthesis tasks, while also using Python and/or R to build and validate machine learning, automation and AI-assisted workflows for searching, eligibility screening, and (meta)data extraction. Your work will also contribute to new meta-research on how, and under what conditions, automated and AI-based tools can speed up and improve evidence synthesis in the environmental field without compromising reliability, robustness or scientific quality.
Our team spans many disciplines and we welcome candidates from different backgrounds. If you have strong research and programming skills, want to help connect sustainability science with policymaking and are keen to learn evidence synthesis methods, we would like to hear from you.
Key tasks and responsibilities
Conduct and support manual literature searching, screening and (meta)data extraction for evidence synthesis projects, working with large volumes of scientific publications and grey literature (including bibliographic information and textual data).
Develop and maintain reproducible workflows in Python and/or R for screening, extraction, classification, quality-checking, cleaning and harmonization of textual and bibliographic data.
Apply and evaluate machine learning, natural language processing and LLM-based methods for text classification, information extraction and document analysis.
Validate automated and AI-generated outputs against human decisions, quantifying errors, uncertainty and potential biases.
Document methods, decisions and validation procedures to support transparent and reproducible research.
Work collaboratively with researchers across environmental, sustainability, climate, energy and social science.
Who you are
You are an early-career researcher with strong analytical and programming skills who enjoys working where sustainability research meets technology development – engaging with both the details of evidence synthesis and the computational methods that support it.
You are happy to do detailed manual eligibility screening and (meta) data extraction, while looking for opportunities to automate these processes and make them more efficient.
You have a background in, or strong interest in, sustainability or environmental research and enjoy learning from people with different disciplinary backgrounds.
You do not need to be an expert in every method or technology used in this role. We value someone who is curious, careful, quick to learn and a constructive team player.
The role would suit someone with:
Experience with more advanced AI development, such as model fine-tuning or deployment, is welcome but deep expertise in machine learning or software engineering is not required.
Experience with any of the following is highly desirable, but not required:
Formal qualifications
Personal skills
Additional information
You will join the International Climate Risk and Adaptation Team, which develops and applies pioneering research methods to define, describe, measure and communicate systemic climate risk in interdependent global systems. Drawing on diverse perspectives, its work spans quantitative and qualitative analysis, climate risk assessment, scenario and foresight development and AI-driven policy analysis.
Our offer
At SEI HQ we offer a stimulating position in an international environment. You will be part of a leading, multinational, multidisciplinary and multilingual team of experts in an organization where the well-being and development of our employees is of high priority. We value diversity and creativity at the core of what we do and we welcome applicants from diverse backgrounds to apply. Our ambition is to provide a safe, professional, and creative workspace for all.
An employment with SEI HQ includes:
Once employed, you must live in Sweden, preferably in the Stockholm area. It is not possible to work from another country.
How to apply
We are reviewing applications on an ongoing basis. Please submit your application as soon as possible, and no later than 26 October 2026, 23:59 Stockholm local time.
Applications must be written in English and submitted through our recruitment system. Please include:
Please note that we screen all applicants manually. Incomplete applications will not be accepted.
