项目介绍

2026年5月29日

The Center for Life Cycle Engineering at the Department of Green Technology, University of Southern Denmark, invites applications for a 3-year PhD position in Artificial Intelligence-Driven Life Cycle Assessment. The position is based in Odense, Denmark, and will contribute to ongoing EU-funded research activities. The position is expected to be available from September 1, 2026, or as soon as possible thereafter.

The PhD project will focus on the development and application of trustworthy AI-assisted approaches for quantitative sustainability assessment, with particular emphasis on Life Cycle Assessment (LCA), environmental foot printing, data quality, transparency, reproducibility, and decision support for emerging bio-based and food-related technologies.

Project Context

The green transition requires robust, transparent, and scalable methods for assessing the environmental performance of emerging technologies. In many innovation projects, however, LCA studies are challenged by fragmented data, uncertain inventories, limited documentation, and the need to assess systems that are still under development.

This PhD project will investigate how artificial intelligence, including large language models, structured data workflows, and digital tools, can support quantitative sustainability assessment while maintaining scientific transparency, traceability, and methodological rigor. The work will be connected to case studies in ongoing EU projects, including emerging bio-based production systems, precision fermentation, novel ingredients, and circular bio-based value chains.

The PhD candidate will contribute to developing AI-assisted workflows for collecting, structuring, validating, and interpreting inventory data for LCA and related environmental assessment methods. The project will also address uncertainty, data quality, methodological consistency, and the responsible use of AI in sustainability assessment.

Main Research Tasks

The PhD student is expected to work on some or all of the following tasks:

• Develop, test, and validate AI-assisted workflows for life cycle inventory data collection, structuring, quality checking, and documentation.

• Investigate the use of trustworthy AI, including large language models, retrieval-augmented generation, and structured knowledge bases, to support quantitative sustainability assessment.

• Develop methods to improve data quality assurance, traceability, transparency, uncertainty handling, and reproducibility in AI-supported LCA and environmental footprint workflows.

• Design and evaluate approaches for reducing unsupported AI outputs and hallucinations, including source verification, evidence tracking, consistency checks, and human-in-the-loop validation.

• Apply and validate the developed methods using relevant sustainability assessment case studies, technology, or sector.

• Develop structured procedures for documenting assumptions, data sources, model choices, data gaps, limitations, and interpretation of results in AI-assisted assessments.

• Contribute to scientific publications, research documentation, collaboration with academic and industrial partners, and relevant teaching, supervision, dissemination, and project meetings to a limited extent.

Qualifications
The successful candidate must have the following qualifications:

• A relevant MSc degree in environmental engineering, sustainability assessment, industrial ecology, chemical engineering, bioengineering, data science, computer science, or a closely related field.

• Documented knowledge and practical understanding of Life Cycle Assessment, environmental footprinting, sustainability assessment, or environmental systems analysis.

• Documented programming experience, for example in Python, R, MATLAB, or similar tools, and the ability to work with quantitative data, scripts, and reproducible workflows.

• Knowledge of artificial intelligence methods and their application to scientific data analysis, environmental assessment, or decision-support workflows.

• Knowledge of large language models, prompt engineering, retrieval-augmented generation, or related AI-assisted approaches, including awareness of risks such as hallucinations, bias, and unsupported outputs.

• Ability to work systematically with scientific data, documentation, assumptions, uncertainty, data quality assurance, transparency, and traceability.

• Excellent written and spoken English skills.

Ability to work in an international multidisciplinary environment is also an advantage. A collaborative mindset, good communication skills, and motivation to contribute to research, teaching, and project-based collaboration are highly valued.

Other Competencies

• Ability to work independently while contributing constructively to a research team.

• Strong analytical, organizational, and problem-solving skills.

• Strong communication and documentation skills, both written and oral.

• Motivation to publish scientific results and contribute to international research projects.

• Interest in contributing to teaching, outreach, or mentoring activities may be considered an asset.

SDU Life Cycle Engineering
The successful candidates will be affiliated with the SDU Life Cycle Engineering, which is acknowledged for its unique research on global, holistic, and systems integration of agriculture, energy, and materials sectors. The Department of Green Technology is located in Odense, the main campus of the University of Southern Denmark. Odense is a fast-growing international community with an open Scandinavian culture, affordable housing and living costs. 

Further information about this position is available from Associate Professor Benyamin Khoshnevisan bekh@igt.sdu.dk.

If you experience technical problems, please contact our email support.

Application

Before applying the candidates are advised to read the Faculty information for prospective PhD students and the SDU information on how to apply.

Assessment of candidates is based on the application material, and the application must include: 

• Motivated application: A clear and concise statement describing your motivation for applying and explaining why you are a strong match for the position.
• Curriculum Vitae. Include detailed information on your previous employment, research and teaching experience, academic background, publications, and personal contact information.
• Master’s and Bachelor’s degree certificates  or equivalent, including transcripts of grades (original and an official English translation). 
• Completed TEK PhD application form for 5+3 applicants. Find the form at the Faculty website.
• Completed TEK PhD form for calculation grade point average. Find the form at the Faculty website. 
• An official document describing the grading scheme of the awarding universities (if not Danish). 
• Only for applicants from programmes that evaluate thesis/examination project by approved/not approved: An official written assessment of the thesis or dissertation project from the grade giving institution. The statement must clearly state that the candidate has been among the top 30 pct. in the graduation class for the study programme.
• List of publications and maximum 2 examples of relevant publications (in case you have any publications).
• Other relevant outputs, e.g. reports, software, data projects, or other outputs that are directly relevant to the position.
• References: Contact information for two academic or professional references is mandatory.