PhD Position Learning and Control for Complex Large-Scale Systems with Applications in Greenhouses

Official university vacancy imported from Delft University of Technology. Details are presented for discovery and may change. Confirm the latest requirements and apply on the official source.

• Airflow control by forced convection to improve photosynthesis efficiency by CO2 delivery to the leaf surface and to couple the crop and climate regulation more tightly;
• Energy-saving strategies where the application of lighting, active ventilation and heating are optimized based on plant performance and energy price fluctuations.
• Methods for reduced-order hybrid model learning, namely control-oriented and transferrable models of airflow dynamics, i.e., CO2 level, temperature, humidity, using microclimate sensor and CFD simulation data from other researchers.
• Optimal sensor and actuator placement, aiming at cost and benefit trade-offs of sensor and actuator layouts, e.g., for improved ventilation.
• Hybrid data-driven and model-based control algorithms for improved online decision-making, e.g., for ventilation performance, possibly using the learned reduced-order models and the spatially distributed sensors.
• Data-driven predictive control design of PDEs based on Koopman operators and/or relying on sparse identification of nonlinear dynamics (SINDy) for model predictive control, e.g., for adapting ventilation (on/off fan operating schedule), heating, artificial lighting strategy and screens, and including energy price fluctuations.
• Research trials and experiments, to validate and iteratively improve the control design, supported by other consortium partners.
• Completed a relevant MSc degree in systems and control, applied mathematics, engineering, or a related field
• A 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 / biological systems is preferred, but not required
• A curriculum vitae (CV) that states your education and relevant working experience
• A motivation letter stating why the proposed research topic interests you, why you want to pursue a PhD degree, and why this PhD position suits you well (no more than 1 page)
• If applicants are selected for an interview, they will be asked to provide the names of two persons and their email addresses who could be contacted for a reference with their consent
• One or two research-oriented documents written by the applicant (e.g., MSc thesis, journal/conference publication)
• Transcripts for your BSc and MSc degrees including grades for courses
• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.