The Leiden Computational Network Science Lab (CNS Lab) researches methods for knowledge discovery from real-world network data.

Real-life processes are characterized by dynamics involving time. Examples are walking, sleeping, disease progress in medical treatment, and events in a workflow.

A major grant for research into machine learning algorithms for quantum computers. With this ERC Consolidator grant, Vedran Dunjko and his colleagues hope to discover which real-world problems a quantum computer can solve faster than a normal one.

This master’s programme combines Physics with Data Science. You will learn how physics has its own tricks to deal with big data and how techniques from machine learning and deep learning can be applied to classical and quantum data. The first focus of attention is on classical data, including data mining,…

At Leiden University, researchers from all disciplines work together to find answers and design innovations in the field of artificial intelligence.

The Faculty of Science

The majority of scientists, from archaeologists through to zoologists, collect huge volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics, we can develop algorithms for analysing and identifying patterns…

Collecting data in sports increased in importance the last few years.

Quantum computing is a novel paradigm for computation, which is nearing real-world impact with the coming generation of limited, but nonetheless powerful quantum devices.

This project will transform OpenML, a platform for sharing and creating datasets and AI models transparently and collaboratively, into a next-generation open science infrastructure.

Proving a quantum computer to be quicker than a normal one is one step closer. After a breakthrough in speeding up classical algorithms, researchers Vedran Dunjko and Casper Gyurik showed that only one quantum algorithm could beat its classical counterpart. They discuss their discovery in Quanta Mag…

The majority of scientists, from archaeologists through to zoologists, collect enormous volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics and computer science, we can develop algorithms for analyzing…

Micro-credentials make education for professionals even more accessible. If you follow a module of 3 to 30 EC (European Credits), you receive a micro credential after completing the education program. This digital certificate is recognized by 22 universities of applied sciences and 12 universities.

Course

Developing new medicines is one of modern science's most significant hurdles, a process marked by high costs, long timelines, and frequent failures of promising candidates.

Book presentation

Cancer is the leading cause of death in the Netherlands, and, with over 100 different types of cancer, it’s not a simple disease. Today, skin, breast, lung, prostate and colon cancer are the most diagnosed forms. Therefore, the discovery and development of new drugs has the ability to significantly…

The archaeological heritage of South America is facing increasing threats due to the expansion of agricultural activities, infrastructure expansion, illegal wood harvesting, and the current fire emergency plaguing the Amazon and other biomes of the continent.

The research programme Statistical Science is concerned with the analysis and interpretation of masses of data, the quantification of uncertainty using probability models, and the development and benchmarking of algorithms and methods with these aims.

In this special edition of Information Polity there is a focus on the transparency challenges of using algorithms in government in decision-making procedures at the macro-, meso-, and micro-levels.

The NEASQC project brings together academic experts and industrial end-users to investigate and develop a new breed of Quantum-enabled applications that can take advantage of NISQ (Noise Intermediate-Scale Quantum) systems in the near future. NEASQC is use-case driven, addressing practical problems…

The project XAIPre (pronounce “Xyper”) aims to develop predictive maintenance system for the maritime industry using sensor technology and artificial intelligence. The project aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction…

You gain a strong foundation in computer science, combined with knowledge of machine learning, cognitive science, human-robot interaction. You will learn to develop and program systems based on knowledge of the human brain.

The NET-ROL project builds on the EU's definition of the rule of law to investigate the role of networks in law-making, executive decision-making, and the judiciary. It examines how these networks influence the weakening of the rule of law. Through an interdisciplinary perspective, the project explores…

This thesis surveys the intersection of computed tomography (CT) and machine learning (ML), treating CT as an ill-posed inverse problem shaped by object properties, imaging physics, and data limitations.

In high school, Tim van Erven read about an artificially intelligent algorithm that could solve mazes. From that moment on, he was sold: ‘There’s something magical about algorithms. With a list of fixed rules you can make them learn the most diverse things.’ This year, he won a Vidi grant, which he…

A unique multicenter study, including about 3,500 youth between 10 and 25 years old from across the globe, shows that artificial intelligence - specifically machine learning - is able to identify individuals with anxiety disorders based on their unique brain structure.

The recently promoted Hao Wang from the Leiden Institute of Advanced Computer Science teaches computer programmes how to solve real-world problems. His goal is to make the process of problem-solving more efficient. On 1 November, he obtained his doctorate with the predicate cum laude. ‘I hope to solve…

A Leiden-Delft-Erasmus research team has been awarded a LDE Global Support Grant to develop reusable algorithms in the remote detection of non-orthogonal architectural features, taking place in the archaeological context of the northern extremities of the Andean, part of the Istmo-Colombian Area.

The results of the investigations by citizens are used in an innovative research project that investigates the potential of machine learning and automated detection in archaeology.

How digital technologies affect the way we conceive of the self and its relation to the world, considered through the lens of media art practices.

Quantum computers are a proposed fundamentally new type of computer. They aim to perform some computations much faster than previously possible by exploiting phenomena at the quantum scale, called superposition and entanglement.

The Explainatory Data Analysis group develops algorithms and theory that enable domain experts to explain data by finding interpretable patterns and models.

The new sports center, which will also be used as an examination center, will be located at the Campus Square.

In the traditional theory of linear elasticity, superposition dictates that the response of a material does not depend on the sequence of the applied mechanical actuations.

The Translation Lab in Huizinga 0.09 hosts research and training activities in various aspects of translation, including audiovisual translation, machine translation and AI, translation process research and user’s reception.

We study cell-fate decision-making using embryonic stem cells as a model system. Stem cells integrate a large number of cues to direct their development into a great variety of cell types.