PhD Position in Artificial Intelligence-driven Life Cycle Assessment

The Center for Life Cycle Engineeringat 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 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 .

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Application

Before applying the candidates are advised to read theFaculty information for prospective PhD studentsand theSDU 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.
• Masters and Bachelors degree certificates or equivalent including transcripts of grades(original and an official English translation).
• Completed TEK PhD application form for 53 applicants. Find the form at theFaculty website.
• Completed TEK PhD form for calculation grade point average. Find the form at theFaculty 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 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 software data projects or other outputs that are directly relevant to the position.
• References: Contact information for two academic or professional references is mandatory.

Applicants may also include reference letters and other supporting documents that demonstrate their qualifications and suitability for the position.

All documents must be in English and PDF format. CPR number (civil registration number) must be crossed out. All PDF-files must be unlocked allow binding and may not be password protected.

SUBMISSION GUIDE: Motivated application must be uploaded under Cover letter (max. 5 MB) Curriculum Vitae must be uploaded under Resume (max 5 MB). All other documents must be uploaded under Miscellaneous documents (max 10 files with a maximum 50 MB per file).

The application deadline isJune 30 2026at 11:59 PM/23:59 (CET/CEST)

Assessment and selection process

Applications will be assessed by an assessment committee. Shortlisting may be applied and only shortlisted candidates will receive a written assessment.Read about shortlisting at and tests may be part of the overall about theAssessment and selection process.

Conditions of enrollment/employment

Appointment as a PhD fellow is a 3-year salaried position and the monthly gross salary incl. pension is 37.075 DKK. If you have relevant postgraduate experience you may be placed on a higher salary step.

Applicants must hold a masters degree (equivalent to a Danish masters degree) at the time of enrollment and is contingent on enrollment approved by the PhD will be in accordance withFaculty regulations and the Danish Ministerial Order on the PhD Programme at the Universities (PhD order).Employment will be in accordance with the collective agreement between the Ministry of Finance and the Danish Confederation of Professional Associations for academics in the state including the associated circular on the job structure for academic staff at Danish universities and the provisions for PhD fellows as described therein as well as the Protocol on PhD fellows signed by the Danish Ministry of Finance and the Danish Confederation of Professional Associations (AC).Further information about salary and conditions of employed in the position may based on a specific individual managerial assessment be exempted from time registration also known as a self-organizer.

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The University of Southern Denmark wishes its staff to reflect the surrounding community and therefore encourages everyone regardless of personal background to apply for the position. SDU conducts research in critical technologies which due to the risk of unwanted knowledge transfer is subject to a number of security measures. Therefore based on information from open sources background checks may be conducted on candidates for the position(s).

Further informationfor international applicants about entering and working in Denmark. You may also visitWorkinDenmarkfor additional information.

Further informationabout The Faculty of Engineering.