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Advanced Biosystems Modelling
This course provides insights into currently used and advanced simulation methods that are designed to understand and predict spatial-temporal phenomena present in many biosystems. The course starts an analysis of PDEs and the relevant numerical schemes. The second part of the course introduces Computational Fluid Dynamics (CFD), whereby the focus will be on engineered biosystems. Finally, models that are discrete in nature, such as, agent-based models that are used to simulate and predict the complex emergent behaviour of many species (cells, organisms) and their interactions.
Analysis: functions of one and several variables
This is a two-semester analysis course for bioscience engineering students in their first year. It covers the main theorems, proofs, formalism and techniques from differential and integral calculus for functions of one and several variables. Students are also introduced to numerical and symbolical calculations in Python. This course uses an in-house developed textbook OSCalculus.
Calculus I and Linear Algebra and Calculus II
This is a two-semester calculus and linear algebra course for engineering students in their first year. It covers the main techniques from differential and integral calculus for functions of one and several variables, and linear algebra. Students are also introduced to numerical and symbolical calculations in Python. This course uses an in-house developed textbook OSCalculus.
Introduction to mathematical modelling
This course provides a basis for the formulation and usage of mathematical models necessary for simulations of pharmaceutical processes. Students are familiarized with systems of differential equations and (non-)linear differential equations, and introduced to analytical and numerical solution methods to be able to predict processes In the second part of the course, students will learn how to deal with modelling uncertainty and probability. Finally students will learn how to perform a sensitivity analysis and parameter estimation of the models, as well as how to decide how "good" a model is.
Mathematics I and II
This is a two-semester calculus course for engineering students in their first year. It covers the main techniques from differential and integral calculus for functions of one and several variables, linear algebra and ordinary first-order differential equations. Students are also introduced to numerical and symbolical calculations in Python. This course uses an in-house developed textbook OSCalculus.
Process Control
Aim of the course is to get gain insight in the necessity of process control systems in modern process operation and the way these control systems are built. Classic feedback control based on PID-controllers are introduced including their tuning and stability analysis. Extensions such as cascade and feedforward control are touched on as well as practical problems such as kick and integral windup. Modern controllers such as state feedback, LQ-control and model-based control are introduced.
Remediëring wiskunde (Isaac en Newton)
These remediation programs are mandatory for students who wish to enroll in the Bachelor's programs of Bioscience Engineering, Bio-Industrial Sciences, or Biosciences but did not pass the 'starttoets'. The remediation program is offered through an online self-study mathematics package and is tailored to the needs of the program. For students in Bioscience Engineering, the topics cover mathematics from a pre-university education with at least 6 hours of mathematics, while for the other programs, the topics cover mathematics from pre-university education with at least 4 hours of mathematics.
Selected topics in mathematical optimization
As an advanced course within the field of applied mathematics, this course focuses on traditional methodologies and recent developments in the area of mathematical optimization. Driven by a variety of applications in bioengineering (including but not limited to bioinformatics), several theoretical concepts on mathematical optimization will be introduced and studied up to a level that allows these concepts to be applicable in practice. Students are taught how to translate (real-life) problems of substantial complexity into formal mathematical optimization problems.