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Education - S (Simon) van Mourik


The following courses are aimed at students that conduct research of biosystems under variable and uncertain circumstances.   

  1. Data analysis (FTE-26306) for BSc students, aimed at finding and analysing data driven evidence. The course is inspired by the Advanced Statistics courses, and tailored to biosystems engineering applications. The underlying model framework is a general linear model, built solely upon data.
  2. Machine learning (FTE-35306) for MSc students. This course is an introduction to automated learning in order to make diagnosis and predictions. The underlying modeling framework is consist of various nonlinear structures, but all are black-box (not based on underlying principles).
  3. Precision Farming (FTE-35806) for MSc students. In this course we build upon classic control engineering theory, and design ways to create methods for precise dosage, timing, and allocation of inputs. In particular, the focus is on prediction, diagnosis, and control. The model framework consists of nonlinear differential equations.
  4. Statistical uncertainty analysis of dynamic models: PhD tutorial. Introduction to uncertainty propagation in dynamic input-state-output models. The model framework is general, to allow nonlinearity, and first principle models. The latter is important for investigating the underlying mechanisms of a system, management design, and systems design. For more info, see



HPP32306 Vertical Farming
FTE79224 MSc Research Practice Agricultural Biosystems Engineering
FTE80424 MSc Thesis Agricultural Biosystems Engineering
FTE80436 MSc Thesis Agricultural Biosystems Engineering
FTE79324 MSc Research Practice Agricultural Biosystems Engineering
FTE31306 Greenhouse Technology
FTE12303 Introduction Biosystems Engineering part 1
FTE35806 Control Methods for Precision Farming
HPP32306 Vertical Farming
FTE80424 MSc Thesis Agricultural Biosystems Engineering