PhD: Learning‑Based Climate Control for Smart Greenhouses

Delft University of Technology; Delft; On-site; EUR 30,000 - 42,000; Full time

Job summary

Delft University of Technology invites applications for a PhD position focused on learning and control in complex large‑scale systems, with applications to autonomous greenhouse control within the GreenControl project.

You will develop reduced‑order, data‑driven and model‑based control methods for climate regulation in controlled environments, study Koopman operators, and validate results on greenhouse demonstrators.

Qualifications

• Completed MSc degree in systems and control, applied mathematics, engineering, or a related field.
• Strong background or interest in systems and control, applied mathematics, machine learning, and affinity with biological systems applications.
• Some experience conducting, designing, and/or managing experiments for physical or biological systems is preferred.

Responsibilities

• Methods for reduced-order hybrid model learning, namely control-oriented and transferable models of airflow dynamics.
• Optimal sensor and actuator placement for cost/benefit trade-offs and improved ventilation.
• Hybrid data-driven and model-based control algorithms for online decision making.
• Data-driven predictive control design of PDEs based on Koopman operators and/or SINDy for ventilation strategies.
• Research trials and experiments to validate and iteratively improve the control design.

Skills

Systems and control

Applied mathematics

Machine learning

Biological systems applications

Experiment design/management

Education

MSc in systems and control

MSc in applied mathematics

MSc in engineering

Job description

Delft University of Technology invites applications for a PhD position focused on learning and control in complex large‑scale systems, with applications to autonomous greenhouse control within the GreenControl project.

You will develop reduced‑order, data‑driven and model‑based control methods for climate regulation in controlled environments, study Koopman operators, and validate results on greenhouse demonstrators.