• Create models able to embed the dynamics and flexibility of processes and related supply chains
• Combine analytical and data-driven surrogate models in optimization
• Implement and solve your models using state-of-the-art optimization engines
• Analyze various options using life-cycle assessment approaches
• Look into the sustainability of alternatives and embed these into decision making
• Collaborate with experts across school borders to support the topical development

• Computer-aided decision making
• Mathematical modeling and optimization
• Experience or interest in artificial intelligence / machine learning
• Good programming skills, e.g. Python or equivalent
• Supply chain planning of chemical processes, energy systems or in another relevant area

• Motivation letter with a short description of your research interest. Please include the applicant’s contact information and an indication of the preferred starting date (max. 1 page)
• CV including details of all academic merits (including publications, if applicable), and contact information of two persons who may be contacted to provide a character reference (max. 3 pages)
• Summary of the applicant’s master’s thesis (max. 1 page)
• Copy of the applicant’s master’s degree certificate and a transcript of studies (with a clear explanation of the grading scale)