Postdoc: Robust Stochastic Optimization for Maritime Logistics

DTU - Technical University of Denmark; Kongens Lyngby; On-site; DKK 640,000 - 720,000; Full time

Job summary

DTU - Technical University of Denmark in Copenhagen hosts the SCRUB project, advancing robust scheduling and routing for bulk carriers under uncertainty. You will craft optimization models, integrate industry data, and contribute to scientific publications.

This role blends theory with real-world impact, anchored in a leading technical university environment. The project emphasizes collaboration, interdisciplinary work, and delivering robust methods for industrial operators, with a focus on

Qualifications

• As part of the SCRUB project, you will develop optimization models and solution algorithms for robust scheduling and routing.
• Strong expertise in mathematical optimization and large-scale algorithm design.
• Knowledge of decision making under uncertainty.
• Exposure to machine learning methods is desirable.
• A passion for research and for solving real industrial challenges.
• Excellent programming and collaboration skills.
• Fluency in English (spoken and written).

Responsibilities

• Develop advanced optimization models and solution algorithms for robust scheduling and routing of bulk carriers.
• Collaborate with industry partners to integrate real data into models.
• Publish research findings and contribute to project deliverables.
• Participate in interdisciplinary team activities and college-wide seminars.

Skills

Mathematical optimization

Large-scale algorithms

Uncertainty decision making

Machine learning exposure

Programming skills

Collaboration

Fluent English

Education

PhD degree

Job description

DTU - Technical University of Denmark in Copenhagen hosts the SCRUB project, advancing robust scheduling and routing for bulk carriers under uncertainty. You will craft optimization models, integrate industry data, and contribute to scientific publications.

This role blends theory with real-world impact, anchored in a leading technical university environment. The project emphasizes collaboration, interdisciplinary work, and delivering robust methods for industrial operators, with a focus on