Optimization-simulation coupling for the GHG emission-based supervision and planification of a fleet of autonomous agricultur...

This role focuses on the development and integration of optimization and simulation techniques aimed at supervising and planning a fleet of autonomous agricultural vehicles with an emphasis on reducing greenhouse gas (GHG) emissions. Key competencies include expertise in optimization algorithms, simulation modeling, and their coupling to create efficient decision-support systems. Candidates should have a strong understanding of autonomous vehicle operations within agricultural contexts and the environmental impact of fleet management. Proficiency in programming languages commonly used for simulation and optimization, such as Python, MATLAB, or similar, is essential. Familiarity with GHG emission metrics, environmental modeling, and sustainability principles is important to align fleet supervision with emission reduction goals. Experience in systems engineering, data analysis, and possibly machine learning techniques to enhance predictive accuracy and operational efficiency would be beneficial. The role likely requires interdisciplinary knowledge combining agricultural engineering, environmental science, and computer science to effectively design and implement supervisory control systems that optimize fleet performance while minimizing environmental footprint. Strong analytical skills, problem-solving abilities, and experience with complex system integration are critical. While specific experience and education levels are not provided, the complexity of the task suggests a preference for candidates with advanced education in relevant fields and practical experience in simulation-optimization frameworks and autonomous systems.