Promotor: Roeland M.H. Merks

Epithelial-mesenchymal plasticity (EMP) and tumor cell migration play an important role in cancer progression, and an improved understanding of the mechanisms underlying these concepts is essential for developing new targeted approaches.

Accelerating the diagnosis and personalising the management of inherited metabolic diseases.

There is a need for alternative methods to replace, reduce and refine (3R) animal experimentation. Combining experimental data from high-throughput in vitro studies with in silico modeling is a promising approach to unravel the effect of chemicals on living cells and to gain a better understanding of…

Promotor: Prof.dr. J.N. Kok

In this project, we aim to obtain a quantitative understanding of the role of the immune system in tumor regression, of the role of tumor cell heterogeneity in cancer growth and of tumor cell migration properties.

In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines.

Drug-target binding kinetics determine the time course of the central event in pharmacotherapy: Drug-target interaction.

Nonparametric Bayes for high-dimensional models: contraction, credible sets, computations

Breast cancer (BC) is a clinically and biologically heterogeneous disease.

Computational linguistics uses computational methods to help answer linguistics questions across a variety of topics.

Promotor: Prof.dr. E. Deprettere

Projects within the theme ‘Computational Biology’ aim to employ mathematical or computational dynamical modelling approaches to discover novel concepts that are important for drug development.

This thesis focuses on two processes involved in fighting infections: metabolism and immune cell motility and navigation.

We investigate reaction systems, a novel computational model gleaned from the biochemical reactions taking place in living cells and information processing in nature.

Immunotherapies for cancer are an emerging class of therapeutic strategies which aim to treat cancer via augmentation of the immune system.

The master's specialisation Bioinformatics at Leiden University focuses on research, development and application of computational tools and approaches for expanding the use of biological, medical, behavioral and health data.

Research in the natural computing group covers theoretical foundations, the development of new algorithms, and interdisciplinary applications of natural computing methods.

Quantum computing represents a fundamentally new computational paradigm, capable of addressing problems that are intractable for classical computers.

A complex community of microbes develops in the infant gut shortly after birth. We call this community the infant gut microbiota. The microbiota influences the health of the infant, which makes the composition and function of the infant gut microbiota an important topic to study.

During the development of the (human) body from a single fertilized egg, organs and structures must form. The development and growth of biological forms is called morphogenesis.

The Computer Systems, Imagery & media (CSI) research programme performs research on methods and techniques for the design, implementation and application of advanced computer systems, in particular parallel, distributed and embedded computer systems.

The goal of the MODOMA-project is to create a computer model of language acquisition.

This thesis covers several aspects of quantum algorithms for near-term quantum computers and its applications to quantum chemistry and material science.

We strive to address the challenge of constructing a modeling language to write software which can take advantage of recent hardware developments (multicore, cloud) without compromising in its abstraction levels.

In this research group, led by Dr. Joost Beltman, the aim is to employ mathematical and computational dynamical modelling approaches in order to quantitatively and mechanistically understand the dynamical behaviour and regulation of intracellular networks of genes, proteins and metabolites as well as…

This thesis probes under what conditions quantum computing presents an advantage over classical computing.

In this project we study the properties of statistical- and rule-learning mechanisms in relation to the acquisition and evolution of language. We ask to what extent these mechanisms are unique to humans - or to human language - by comparing the acquisition of vocal structure in two species: humans (infants)…

In this thesis, Quantum computing, norms, and polynomials, we investigate the interplay between quantum mechanics, complexity theory, and functional analysis, three central areas of physics, computer science, and mathematics, respectively.

Promotor: Prof.dr. S. Haring

Research in the Natural Computing and Optimization cluster covers theoretical foundations, the development of new algorithms, and interdisciplinary applications of natural computing methods.

We see computer programming as an essential skill. It enables you to be self-sufficient in building tools, processing data, visualizing research output, communicating research results, etc. Moreover, it empowers you to make beautiful things.

In this theme you will be supplied with a firm background in the core of current computational linguistics and a solid comprehension of its neighboring fields.

Every day, we make numerous decisions, from choosing what to eat to how we interpret the world around us. Traditionally, researchers have assumed that a key part of our decision-making process, namely the decision criterion, stays stable over time. However, increasing evidence suggests that instead…

Computational thinking, programming, and algorithms form the basic of many of the things that are shaping our world, the way we work, interact, organize ourselves, and even spend our free time with social networks, streaming media, and games. And this transformation is far from being complete, it is…

This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…

Uncertainty and noise are frequently-encountered obstacles in real-world applications of numerical optimization. The practice of optimization that deals with uncertainties and noise is commonly referred to as robust optimization.

This dissertation investigates Extended Lexical Units (ELUs), elements that are bigger than just one word and which are stored in the Lexicon.

Matthijs van Leeuwen of the Leiden Institute of Advanced Computer Science develops methods to make computer models interactive. With interactive models, experts can combine information from raw data with their own knowledge to make predictions more accurate. 'In this way we hope to build models that…

This dissertation focused on computational methods based on first principles calculations using the Density Functional Theory (DFT) framework. Emphasis was laid on affordable methods that can provide a tradeoff between computational expense and accuracy.

Are you thinking about studying Computer Science? Learn more and watch the videos. Note: As of September 2025 you can also combine this master programme with the Business Studies specialisation.

In this work we describe three methods to improve the performance of Quantum Field Theory calculations.

Research in this group, headed by Gerard van Westen, focusses on computational methods integrated in different parts of the drug discovery process. More specifically, topics include innovative treatments for cancer, selectivity modeling, translational research, allosteric modulation, drug resistance…

Sonja Boas developed new techniques to study vascular growth computationally. The results of her research provide a better understanding into mechanisms behind vascular growth, which is relevant to the development of medical therapies. She defends her thesis 'Computational modeling of angiogenesis'…

In this thesis, we investigate how deep generative models (AI models that can create new designs) can be better utilized for technical and industrial design. Our goal is to generate designs that are not only realistic (plausible), but also efficient, reliable, and usable in complex 2D and 3D applica…

PhD candidates carry out a programme of independent research and additional (limited) course work, culminating in production of a PhD thesis in typically 4 years.

This thesis describes the importance of being able to control the selectivity of potential drug candidates.