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

.jobdescription td { padding: 0 5px; } .jobdescription * { border: none !important; border-width: 0 !important; font-size: 12px !important; font-family: Arial, Helvetica, sans-serif !important; color: black !important; } .jobdescription h1 b, .jobdescription h2 b, .jobdescription h3 b, .jobdescription h4 b, .jobdescription h1, .jobdescription h2, .jobdescription h3, .jobdescription h4 { font-size: 14px !important; font-weight: bold; margin-bottom: 0.5em !important; } #search-wrapper{display:none;} /* TUDelft Sidebar style */ .jobColumnTwo .joblayouttoken { background: #e3e3e3; color: #011b3c; padding: 0 20px 5px !important; } .jobColumnTwo .joblayouttoken .joblayouttoken-label { color: #039fd8; font-weight: bold; } .jobColumnTwo .joblayouttoken.marginTopLarge { padding: 20px 20px 5px 20px !important; } .jobColumnTwo .joblayouttoken.marginBottomLarge { padding: 0 20px 20px 20px !important; } /* TU Delft full-width header image */ .topleft.verticallyscaled.backgroundimage.large-image-component { height: 0 !important; } div[role='img'] .topleft.scaled.large-image-component{ height: 0 !important; position: absolute; padding-top: 20% !important; background-position: center !important; top: 50px; } div.job{ position: relative; padding-top: 26% !important; } /* Fix voor niet werkende knop */ div.jobTitle{ z-index: 2; position: relative; margin-top: 5px; } PhD Position Learning and Control for Complex Large-Scale Systems with Applications in Greenhouses Job description Recent trends in Controlled Environment Agriculture (CEA) development, design, and operations related to energy saving and greenhouse gas emission reduction, are mainly focused on the ventilation process, which typically considers the use of window and mechanical air treatment based on average climate measurements. Airflow affects crop transpiration, growth, development, yield and quality, but despite its importance, related control strategies in practice are often very crude and rule-based without incorporating any complex plant/microclimate interactions or economic considerations. The state-of-the-art approaches in optimal climate control of greenhouses are based on implementing economic objective functions exploiting a time scale decomposition between short-term climate control/energy use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use of average climate measurements, which are then used to control the overall climate setpoints. Awareness of micro-climate insight and fine-grained, model-based control of locally applied ventilation is lacking in all these approaches. This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the GreenControl project, whose primary objective is to address the above mentioned shortcomings in autonomous greenhouse control. The project team includes PhD students and researchers at TU Delft, Wageningen University, University of Twente, and TU Eindhoven, as well as industrial partners that specialize in greenhouse design and installation, plant breeding, climate control, sensing and monitoring with microdevices, software developers, and technology providers for high-tech greenhouses. The goal of the GreenControl project is to collaborate with this team to support the transition to climate neutrality in CEA by using a completely new operating philosophy that puts plants at the center of the control strategy to achieve 25% energy savings and 35% reduction in energy costs. We will accomplish this by moving from average climate control to direct crop-centric control. This paradigm shift relies on breakthroughs in microclimate sensing, interpreting crop performance by integrating sensor data at different temporal and spatial scales into a crop modelling framework and predicting daily targets for photosynthesis and transpiration rates (all developed by other researchers in the project consortium). Based on these targets and fluctuating electricity prices, the main objective will be to develop a control-oriented model and algorithm to alter the lighting, CO2 dosing, and air circulation that satisfy the crops' needs, while minimizing the resources used and costs. The developed control scenarios will investigate targets with increasing complexity, i.e., daily respiration target, daily photosynthesis target, cost and energy use optimization, and will be improved iteratively culminating in validation trials and experiments. In the GreenControl project, the primary aim is to demonstrate the added value of being able to capture such microclimate effects, via new sensing and modelling approaches developed by our partners, and adjust the control strategies to improve photosynthesis efficiency while reducing overall energy use. Research by our project partners will show how crops respond to microclimate setpoints based on detailed sensing data, and microclimate and crop models, obtained both through mechanistic and CFD-based approaches. These provide the basis for learning reduced complexity control-oriented models that will be exploited by two control strategies: 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. The learned models and resulting data-driven control approaches will be designed to be more easily transferable between different greenhouses than constructing detailed CFD models for each location. Key expected innovations in this project are expected to include a hybrid data-driven and model-based predictive control approach that uses Koopman operators, plant imaging, and 3D microclimate sensors to enable crop control instead of only indirect climate control. The Koopman operator formalism offers applicability in data-driven settings for the analysis and control of large classes of nonlinear and high-dimensional systems, such as air flow dynamics in complex greenhouse environments. In this PhD project, you will explore and conduct research on the intersection of learning theory, PDEs, and systems & control, likely using RKHSs (or similar function spaces), Koopman operators, and neural networks to study interesting classes of controlled PDEs, develop suitable learning schemes, and design control policies accordingly. You will use large-scale optimization for the implementation of the obtained results, first using high-fidelity numerical simulations, and then implemented and verified on a greenhouse demonstrator. The main research and development tasks include: 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. Job requirements 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 TU Delft (Delft University of Technology) Working at TU Delft means contributing to solutions that really make a difference. For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future. At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward. Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems. ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation. . Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work. Conditions of employment Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills. The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged. Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to