PhD in Data-driven Critical Raw Material Supply Chain Resilience

University of Southern Denmark; Odense; On-site; DKK 377,000 - 451,000; Full time

Job summary

University of Southern Denmark invites applications for a three-year PhD position in data-driven critical raw material supply chain resilience. Based in Odense, the PhD will develop modular, transparent computational modules for dynamic material flow analysis and assess supply-chain resilience in wind energy and data infrastructure.

The project blends environmental systems analysis with data science and network analysis.

Qualifications

• Strong background in quantitative analysis, system modelling and data science.
• Experience programming in Python or a similar language.
• Experience with data handling: cleaning, transformation and exploratory analysis.
• Ability to work independently and collaboratively in an interdisciplinary environment.
• Good written and spoken English.
• Strong interest in sustainability and supply-chain resilience.

Skills

Programming

Data analysis

Quantitative methods

English proficiency

Collaborative work

Education

Master's degree in a relevant field

Job description

PhD Position and Research Environment

Do you want to investigate the resilience of critical raw material supply chains supporting low‑carbon and digital technologies? Join us as a PhD student at SDU Life Cycle Engineering, University of Southern Denmark. The Department of Green Technology at the University of Southern Denmark invites applications for a three‑year PhD position in data‑driven critical raw material supply chain resilience. The position will be based at SDU Life Cycle Engineering, Department of Green Technology, Faculty of Engineering, at the SDU Odense campus. The expected starting date is 15 February 2027, or as soon as possible thereafter.

The deployment of low‑carbon technologies, electrification and digital infrastructure is increasing global demand for critical raw materials. At the same time, the production and processing of many of these materials are geographically concentrated and exposed to geopolitical, economic, environmental and logistical disruptions. Understanding how technology development affects material demand, in‑use stocks, end‑of‑life flows and global supply dependencies is therefore important for assessing and strengthening the resilience of critical raw material supply chains.

The PhD position is jointly funded through research projects on critical raw material supply chains and material requirements for Denmark’s climate‑neutral transition. The project will be embedded in an interdisciplinary research environment combining environmental systems analysis, supply‑chain research and data science. The PhD project will develop modular and data‑driven approaches for analysing critical raw material demand, supply‑chain dependencies and resilience. A central methodological contribution will be the development of transparent, reusable and reproducible computational modules for dynamic material flow analysis.

The modules will support the integration of technology deployment scenarios, material intensities, product lifetimes, stocks and flows, end‑of‑life management, recycling, production, trade, facility and ownership data. The developed approaches will be applied to selected technology systems, including Denmark’s wind‑energy infrastructure and the rapidly expanding infrastructure supporting data centres and AI computing. These cases will be analysed within a common methodological framework to examine how infrastructure expansion and technological change influence demand for critical and scarce materials, in‑use stocks, future waste flows, recycling potential and global supply‑chain dependencies. The project may also investigate overlapping material requirements and competition for materials across low‑carbon and digital technologies. Particular attention may be given to supply concentration, host–by‑product dependencies, trade and ownership structures, supply bottlenecks, cascading risks and potential resilience strategies. Depending on the candidate’s academic background and the specific research questions, the project may include network analysis, geospatial analysis, scenario modelling, uncertainty analysis, machine learning and other AI‑supported approaches. The candidate will build on selected existing models, datasets and research activities in the group and further develop scientifically robust and reusable methods that improve transparency, comparability and reproducibility in material‑system research. The exact research focus and detailed PhD plan will be developed jointly with the successful candidate, taking into account the project objectives, academic background and research interests.

In addition to a PhD dissertation and high‑quality scientific publications, the project is expected to produce reusable modelling methods, documented datasets and reproducible computational workflows.

Qualifications

We are looking for a highly motivated, independent and collaborative candidate with a strong interest in critical raw materials, quantitative modelling, data‑driven research and sustainability. Applicants must hold a relevant master’s degree or equivalent by the time of enrolment.

Relevant Academic Backgrounds

• Engineering
• Industrial ecology
• Sustainability science
• Resource management
• Data science
• Computer science
• Applied mathematics
• Operations research
• Supply‑chain management
• Geoinformatics
• Mining or geological sciences
• Another relevant natural science or quantitative discipline

Required Skills and Experiences

• A solid academic foundation in quantitative analysis, system modelling, data science or related methods.
• Experience with programming and data analysis, preferably using Python or a similar programming language.
• Experience with data handling, including data cleaning, transformation, integration and exploratory analysis.
• Strong analytical and problem‑solving skills.
• The ability to work independently and collaboratively in an interdisciplinary research environment.
• Good written and spoken English.
• A strong interest in sustainability, critical raw materials and supply‑chain resilience.

Areas of Advantage

• Material flow analysis, industrial ecology or material systems modelling.
• Network, supply‑chain or geospatial analysis.
• Scientific computing and reproducible research workflows.
• Statistics, machine learning, artificial intelligence, optimisation, uncertainty or scenario analysis.
• International trade, production facility or corporate ownership data.
• Critical raw materials, wind energy, digital infrastructure or data centres.

Salary and Employment Terms

The application deadline is November15,2026, at 11:59PM/23:59 (CET/CEST). Appointment as a PhD fellow is a 3‑year salaried position, and the monthly gross salary incl. pension is 37.075DKK. If you have relevant postgraduate experience, you may be placed on a higher salary step. The place of employment is in Odense, Denmark and preferred starting date is February15,2027, or as soon as possible thereafter. Applicants must hold a master’s degree (equivalent to a Danish master’s degree) at the time of enrollment and employment. Employment is contingent on enrollment approved by the PhD School. Enrollment will be in accordance with Faculty 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). Salary is determined in accordance with the applicable collective agreement and based on objective and gender‑neutral criteria, including the content of the position, responsibilities, and qualification requirements. As an applicant, you are entitled to information about the starting salary and salary range for the position. This information will be provided during the recruitment process. SDU does not collect information on applicants’ previous salary. Further information on salary and taxation. Persons employed in the position may, based on a specific individual managerial assessment, be exempted from time registration, also known as a “self-organizer”. 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).

Contact

Further information about the scientific content of the position is available from: Wu Chen, Associate Professor, Department of Green Technology, SDU Life Cycle Engineering, University of Southern Denmark. Email: [e-mail]