Job description
High-tech greenhouses play a crucial role in ensuring sustainable, affordable, and reliable local food production. The construction and operation of high-tech greenhouses is therefore expected to grow significantly over the next decade, but this increase is not matched by a commensurate increase in the number of growers capable of effectively managing them. Achieving high resource-use efficiency of the greenhouse, e.g., maximizing the kg of food produced per unit of energy, requires growers to consider a complex relationship between the short and long term consequences of each operational decision. These factors have lead to a significant interest in autonomous systems for greenhouse management.

Although autonomous greenhouse systems have achieved remarkable improvements in performance over the last couple of years, the current technology cannot be scaled to the entire greenhouse industry as current methods require either enormous amounts of operational data or high fidelity simulators. In addition, current autonomous greenhouse control systems focus almost entirely on the highest level of decision making, while ignoring the lower control layers that actually implement these high-level decisions. This approach leads to a control architecture that is poorly integrated and lacks flexibility in responding to short term perturbations, thereby producing suboptimal performance in terms of profits, resource efficiency, and carbon footprint.

This PhD position is a part of the LEAP-AI project that will address these challenges in autonomous greenhouse control. The project team includes PhD students and researchers at TU Delft, Wageningen University and Research, and Erasmus University Rotterdam, as well as industrial partners that specialize in machine learning, climate control, and computer vision for high-tech greenhouses. The goal of the LEAP-AI project is to collaborate with this team to design the next generation of autonomous greenhouse control systems and will culminate with several large-scale experiments including both research trials as well as validation/demonstration of the new autonomous greenhouse control system in an actual high-tech greenhouse.

In this PhD project, you will explore how physics-informed graph neural networks (GNNs) in combination with nonconventional sensors (such as computer vision) can be used to enable generalizable, explainable, and data efficient machine learning for crop-greenhouse systems. A particular focus will be placed on using these GNNs in predictive and optimization-based control methods with opportunities to explore tailored control formulations and optimization algorithms for optimal control with GNN models. The ultimate goal is to use these insights to redesign the control layers and communication strategy of current autonomous greenhouse systems to improve both the flexibility and explainability of the system.

Job requirements
Applicants should have:

• Completed a relevant MSc degree in systems and control, engineering, applied mathematics, or a related field.
• A strong background or interest in systems and control, machine learning, and biological systems.
• Some experience conducting, designing, and/or managing experiments for physical/biological systems is preferred, but not required.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

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.

Click here to go to the website of the Faculty of Mechanical Engineering. 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 € 2872 per month in the first year to € 3670 in the fourth year. 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.

For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

Additional information
For more information about this vacancy, please contact Dr. Koty McAllister, r.d.mcallister@tudelft.nl.

Application procedure
Are you interested in this vacancy? Please apply no later than 26 January 2025 via the application button and upload the following documents:

• A curriculum vitae (CV) that states your education and relevant working experience.
• A motivation letter stating why the proposed research topic interests you (no more than 1 page).
• The names of two persons and their email addresses who could be contacted for a reference.
• One or two research-oriented documents written by the application (e.g., MSc thesis, journal/conference publication).
• Transcript for your MSc degree including grades for courses.

You can address your application to Koty McAllister.
For more information about the application procedure, please contact Giedo Kocken, HR advisor, recruitment-me@tudelft.nl.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

Please note:

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening can be part of the selection procedure.
• For the final candidates, a knowledge security check will be part of the application procedure. For more information on this check, please consult Chapter 8 of the National Knowledge Security Guidelines. We carry out this check on the basis of legitimate interest.
• Please do not contact us for unsolicited services.