CWI highly values education. CWI researchers not only supervise and inspire the CWI PhD students, but also aim at contributing to teaching Master's students at universities. See the overview of CWI's current involvement in teaching courses in the Netherlands below.
CWI is a research institute and does not have education as an institutional task. However, many of the senior researchers of CWI contribute to education via an affiliation or part-time appointment at one of the Dutch universities.
The courses they teach are listed below in chronological order, starting with the earliest upcoming course.
This course focuses on the algorithmic aspects of stochastic operations research, with a particular emphasis on Markov processes and queueing theory. It explores both exact and approximate solutions for performance measures, such as the mean and distribution of customer numbers and sojourn times in various systems.
Computational models are increasingly useful in modern neuroscience. In this course, we will cover advanced methods for analysis and simulation of neural systems looking at both biophysical and cognitive perspectives. We will focus on models of neural dynamics (using dynamical systems and biophysical models), perception and cognition (using artificial neural networks and brain-inspired deep learning approaches). The course will also contain a strong component of practical ‘hands-on’ work by the students – which will implement computational models during the course.
A major claim to fame of quantum computers is that they will be able to solve some problems significantly faster than believed to be possible on a classical computer. The design of further quantum algorithms is an exciting active field of research, and there is still much left to explore in understanding which problems can be sped up by quantum computers, and by how much.
In this course, students will learn about various advanced topics in quantum algorithms and algorithm design, as well as techniques for understanding for which problems it is possible to get a quantum speedup, and by how much.
In this course we build the theory of Schur-Weyl duality, highest-weight vectors, Clebsch-Gordan and Kronecker coefficients, moment polytopes, and symmetric polynomials, and then study various areas of applications including quantum information theory and quantum algorithms, optimization, and algebraic complexity theory.
Additive combinatorics is a relatively new area of mathematics that brings together topics such as combinatorial and analytic number theory, Ramsey theory, harmonic analysis, graph theory, extremal combinatorics, algebraic methods, ergodic theory and probability theory. The area has strong connections with theoretical computer science, including quantum computing. A centerpiece result is the Green-Tao theorem, which asserts that the prime numbers contain arbitrarily long arithmetic progressions. A general theme that drives much of the area has to do with relating quantitative notions of approximate algebraic structure.
The mathematical reconstruction and processing of images is of fundamental importance in state of the art applications in health and geosciences, e.g. in medical tomography, in high-resolution microscopy or in geophysical inversion.
This course is about inverse problems in imaging. overs the full chain of solving inverse problems in imaging, namely Problem identification → Modeling and discretization → Analysis → Numerical optimization
In this course we focus on the fundamental ideas, theoretical frameworks, and rich array of mathematical tools and techniques that power machine learning.
