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

About the position

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

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.

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.

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 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.

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.

Job requirements

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.

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)

What you’ll bring

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

At a glance

Position type

PhD

Institution

TU Delft (Delft University of Technology)

Department

Not stated

Research group

Not stated

Location

Netherlands

Supervisor / contact person

Dr. Mohammad Khosravi

Funding

Salaried
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%.
The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution.
Salary range:
€3059 - €3881

Duration

Not stated

Expected start

Not stated

Vacancy reference

Not stated

Research focus

Discipline

Mechanical Engineering

Research area

Not stated

Methods

Not stated

Software

Not stated

Dates to know

Listed on FOSS Positions

23 Sep 2026

Original advertisement date

Not stated in the source

Application deadline

30 Sep 2026 Timezone not stated. Check the original advert.

Priority review date

Not stated

Last checked

23 Sep 2026

Last updated

23 Sep 2026

We will not process applications sent by email and/or post.

How to apply

Apply now »
Energy-saving strategies where the application of lighting, active ventilation and heating are optimized based on plant performance and energy price fluctuations.
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.
Application procedure
Are you interested in this vacancy? Please apply no later than 30 September 2026 via the application button and upload the following documents:
You can address your application to Prof.dr.ir. Tamas Keviczky.
You can apply online. We will not process applications sent by email and/or post.
Apply now »

https://careers.tudelft.nl/job/Delft-PhD-Position-Learning-and-Control-for-Complex-Large-Scale-Systems-with-Applications-in-Greenhouses-2628-CD/1366003757/

Contact: t.keviczky@tudelft.nlmohammad.khosravi@tudelft.nl

Original sources

https://careers.tudelft.nl/job/Delft-PhD-Position-Learning-and-Control-for-Complex-Large-Scale-Systems-with-Applications-in-Greenhouses-2628-CD/1366003757/ ↗

https://careers.tudelft.nl/talentcommunity/apply/1366003757/?locale=en_US ↗