Research associate: AI

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: Programming and data analysis skills in Python and/or R, including version control with GitHub Experience handling large volumes of structured and unstructured text Practical experience applying machine learning, NLP or large language models to text classification, information extraction or eligibility screening An interest in evidence synthesis methodology Knowledge of, or interest in, sustainability or environmental research 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: Literature reviews, including systematic, scoping and rapid reviews Working with bibliographic datasets Literature eligibility screening or (meta)data extraction Formal qualifications A master’s degree in data science, computer science, engineering, socio-environmental science, sustainability science or a related field. Early-career experience (1–2 years) in academic or applied research involving data analysis and AI/data-driven methods. Personal skills Collaborative: comfortable working in teams and across disciplinary boundaries. Analytical and critical: able to assess evidence and critically evaluate AI-generated results. Systematic and data-oriented: enjoys turning complex information into reliable datasets. Clear communicator: able to explain technical concepts to non-technical audiences. Curious and adaptable: interested in learning and applying rapidly developing AI methods. Quality-focused and responsible: attentive to research integrity, transparency, uncertainty and AI-related risks. 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: Collective Agreement incl. occupational pension and many other collectively agreed benefits An annual healthcare contribution and additional benefits connected to promoting our employees’ wellbeing such as yearly health check-ups Opportunities for professional growth and development Flexible working hours, 37.5-hour workweek and a generous amount of vacation days Being part of tackling environmental and development challenges and developing solutions for a sustainable future for all. 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: Cover letter highlighting relevant qualifications and experience (no more than two pages) Curriculum vitae (no more than four pages) Please note that we screen all applicants manually. Incomplete applications will not be accepted.