Promotor: Prof.dr. A. Doelman, Co-promotor: V. Rottschäfer

Promoter: Ed F. Deprettere, Co-promoter: Todor P. Stefanov

Promotores: S.J. Edixhoven, P.Parent

Promotor: Prof.dr. S.F. Portegies Zwart

Promotor: Prof.dr. K.J. Batenburg

Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects.

In this thesis, topics relating to the optimization of high-throughput pipelines used for imaging are discussed. In particular, different levels of implementation, i.e., conceptual, software, and hardware, are discussed and the thesis outlines how advances on each level need to be made to make gains…

Optimally solving real-world expensive Black-Box Optimization (BBO) problems w.r.t. real-world constraints, such as wall-clock time and computational costs, is extremely difficult and tedious.

Automated Machine Learning (AutoML) is widely used to automatically build a suitable practical Machine Learning (ML) model for an arbitrary real-world problem, reducing the effort of practitioners in the ML development cycle for real-world applications. Optimization is a key part of a typical AutoML…

Promotor: M.T.M. Koper, Co-Promotor: L.B.F. Juurlink

Optimization tasks in practice have multifaceted challenges as they are often black box, subject to multiple equality and inequality constraints and expensive to evaluate.

In this thesis, we study different curved single crystals because of the diversity of surface structures across their curvature.

The focus throughout this thesis will be on gathering fundamental studies of the detailed structure and composition of the electrode/electrolyte interface effect on the rate and mechanism of key electrocatalytic reactions.

The endocannabinoid receptors CB1R and CB2R are involved in a plethora of processes, and consequently are involved in many pathological conditions. Their wide distribution makes the CBRs both an interesting therapeutic target and hard to study.

For optimization problems, it is often unclear how to choose the most appropriate optimization algorithm. As such, rigorous benchmarking practices are critical to ensure we can gain as much insight into the strengths and weaknesses of these types of algorithms.

Reinforcement learning is a framework that enables agents to learn in a manner similar to humans, i.e. through trial and error. Ideally, we would like to train a generalist agent capable of performing multiple tasks and achieving various goals.

Promotor: Prof.dr. W.T.F. den Hollander

Understanding interactions in quantum many-body systems remains one of the most profound and difficult challenges in condensed matter physics.

Building a Deep Cross-media Transfer Network to extract visual relations that relieve the problem of insufficient training data for visual tasks.

This thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis.

This thesis applies data mining in commercial settings in the telecommunications industry.

Machine Learning is becoming a more and more substantial technology for industry.

Promotor: R.D. Gill, F. Taroni

Promotor: K.E. Schalm, Co-promotor: S.S. Lee

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics.

Als kennisinstellingen, gemeente, burgers en andere partijen of organisaties elkaar onvoldoende weten te vinden, komt kennis niet voldoende ten goede aan de samenleving. Het Leiden Healthy Society Center heeft de ambitie om kennis, beleid en praktijk duurzaam met elkaar te verbinden. Zo willen we onderwijs…

We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry.

Constraint multi-objective optimization with a limited budget for function evaluations is challenging. This thesis tackles this problem by proposing new optimization algorithms. These algorithms are applied on holistic ship design problems. This helps naval architects balance objectives like cost, efficiency,…

Promotor: R. Tijdeman, Co-promotor: H.J.J. te Riele

Op 11 november 2021 verdedigde Mariana Gkliati het proefschrift 'Systemic Accountability of the European Border and Coast Guard'. Het promotieonderzoek is begeleid door promotoren prof.dr. P. Rodrigues en prof.dr. L. Besselink (UvA).

Dappere Kleintjes doet onderzoek naar het voorkomen en behandelen van angstproblemen bij angstgevoelige kinderen van 3-6 jaar. Bezoek onze website voor meer informatie over onze onderzoeken en over wat wij mogelijk voor u kunnen betekenen.

Peter Hoppenbrouwers

This dissertation presents the synthesis and evaluation of antibody recognition for various capsular polysaccharide (CP) fragments of Staphylococcus aureus (S. aureus).

Promotor: Roeland M.H. Merks

In this thesis, we show how MRFM can usefully contribute to the field of condensed-matter.

Assessing the risks of engineered nanomaterials (ENMs) solely on the basis of experimental assays is time-consuming, resource intensive, and constrained by ethical considerations (such as the principles of the 3Rs of animal testing). The adoption of computational toxicology in this field is a high p…

For each living organism health is ensured by correct functioning of its cells. Cells therefore have elaborate methods for regulation of their proteins.

A convolutional neural network (CNN) is a biologically inspired algorithm, highly capable at processing images and videos.

Promotor: Prof.dr. J.N. Kok, Co-promotor: F.J. Verbeek

Promotor: F. Arbab, R. De Nicola, Co-Promotor: M. Tribastone

Promotor: S.J. Edixhoven, Co-promotor: K.J. Batenburg

In this thesis, we address two problems concerning the tautological rings of the moduli space of curves.

On a structural level, the properties featured by a majority of mechanical metamaterials can be ascribed to the finite number of soft internal degrees-of freedom allowing for low-energy deformations.

Promotores: J. Kok, M. Chaudron (Chalmers University)

The thesis describes experimental steps towards reduction of friction on the macroscopic scale by scenarios of thermo- and superlubricity well-known on the nanoscale.

In this dissertation (titled: Exploring the Synergies between Transfer in Reinforcement Learning and Procedural Content Generation) we explore how the two research fields named in the title, namely Transfer in Reinforcement Learning (TRL) and Procedural Content Generation (PCG) can synergize togethe…

Binnen Dappere Kleintjes werken we aan verschillende projecten om peuters en kleuters te helpen die snel angstig of verlegen zijn. We richten ons op het vroeg herkennen van angstig gedrag én op het ondersteunen van ouders om hier mee om te gaan.