PhD Position in Explainable AI for Autonomous Greenhouse Control

**Job Description**
This Ph.D. position is part of the LEAP-AI project, which aims to address challenges in autonomous greenhouse control. The project focuses on designing the next generation of autonomous greenhouse control systems to ensure sustainable, affordable, and reliable local food production. Current autonomous systems struggle with scalability due to high data or simulator requirements, and they often ignore lower control layers, leading to suboptimal performance. This Ph.D. project will explore formal methods from computer science (program synthesis, probabilistic programming) and control systems analysis to derive human-understandable strategies from AI-based data-driven strategies. The goal is to determine the long-term strategies of AI controllers, connect short-term actions to long-term effects, and explain them to growers, ultimately designing alternative growth control strategies to improve system flexibility and explainability.

**Skills & Abilities**
• Strong background or interest in formal methods in computer science (program synthesis, probabilistic programming).
• Strong background or interest in systems and control.
• Strong background or interest in machine learning.
• Strong background or interest in biological systems.
• Experience conducting, designing, and/or managing experiments for physical/biological systems (plus, not required).

**Qualifications**
Required Degree(s) in:
• Computer science
• Systems and control
• Engineering
• Applied mathematics
• A related field

**Experience**
Other:
• Experience conducting, designing, and/or managing experiments for physical/biological systems is a plus, but not required.

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