Master projects
Here you can find all our available master projects.
Open Projects (6)
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Data-driven modeling of preconditioners
Ignition Computing is developing a toolbox to speed up the solving of sequences of systems of linear equations. Solving such a system is at the core of many computationally expensive multi-physics simulations, such as those arising in Computational Fluid Dynamics (CFD).A central challenge in …
Vlado Menkovski
More infoIWIris van der Werf -
Scaling granular material simulations with deep geometric generative models (TAKEN)
Granular materials are one of the world’s most widely used and manipulated materials, only behind water. The modelling of these materials is relevant to various sectors, including energy production, agriculture, cosmetics, construction, and the pharmaceutical industry. Granular materials are collections of discrete particles, which …
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Vlado Menkovski
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High-Quality Long Time Series Generation with Deep Generative Models: Exploring Flow Matching and Exogenous Data Integration (TAKEN)
Time series data—prevalent in finance, healthcare, and climate science—requires advanced generative models for tasks like data augmentation, anomaly detection, scenario planning, and synthetic data generation. Deep Generative Models (DGMs), such as Diffusion Models (DMs) and Flow Matching (FM) models, have emerged as powerful tools …
Vlado Menkovski
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Mahdi Mehmanchi
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Nuclear fusion: Coupling 1D and 2D simulation of Tokamk Plasmas with deep generative models (TAKEN)
[For this project there is a possibility for a 6 months internship at DIFFER (Dutch Institute for Fundamental Energy Research) located at the TU/e campus in Eindhoven. The internship is planned after the preparation phase of the graduation project conditioned on the results achieved …
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Vlado Menkovski
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Simulation of bubbly flows with deep generative modeling (FINISHED)
The topic of the project is simulation of bubbles with deep generative models. Bubbles are a fascinating phenomenon in multiphase flow, and they play an important role in chemical, industrial processes. Bubbles can be simulated well with a first-principle physics simulator based on the …
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Vlado Menkovski
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Atom-Level Generative Modeling of Metal-Organic Frameworks with Graph Transformers (FINISHED)
Metal-organic frameworks (MOFs) are crystalline, porous materials with modular architectures and vast structural diversity, making them ideal candidates for data-driven materials discovery. In recent years, generative machine learning models have been developed to explore the MOF design space by assembling frameworks from pre-defined building …
Vlado Menkovski
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Marko Petkovic
Assigned Projects (4)
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Deep Removal of Active Galactic Nuclei [Taken]Oct 2026
[This project is no longer available]Almost all massive galaxies harbor a central supermassive black hole (SMBH). As these SMBHs grow by accreting surrounding gas and dust, they liberate tremendous amounts of energy, becoming visible as Active Galactic Nuclei (AGN). While the AGN phase is …
Vlado Menkovski
More infoLWLingyu Wang -
Neural surrogate modeling of biomolecular condensate morphology [Taken]Sep 2026
[This project is no longer available]Mapping the phase behavior of biomolecular condensates across a multi-dimensional parameter space is a fundamental challenge in soft matter science, relevant to materials design and drug delivery. Automated platforms can navigate this space using active machine learning, but current …
Vlado Menkovski
More infoNENadia Erkamp -
Simulating the Cosmic Web: Flow Matching Generative Models for Large-Scale Cosmological Structures [Taken]Sep 2026
[This project is no longer available]The large-scale structure of the universe is governed by the gravitational evolution of dark matter, forming an intricate cosmic web of filaments, expansive voids, and massive galaxy clusters. High-resolution N-body simulations, such as the Quijote suite, are the standard …
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Vlado Menkovski
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Diffusion models for high-resolution probabilistic weather modelling [Taken]Sep 2026
[This project is no longer available]The domain of weather forecasting is currently undergoing a significant transformation driven by advances in machine learning, where Data-Driven Models (DDMs) have demonstrated equal or superior performance compared to traditional Numerical Weather Prediction (NWP) models in predicting various variables, …
Vlado Menkovski
More infoSBSophie Buurman
Finished Projects (43)
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Sustainable Large Language ModelsDec 2024
Project description:Large Language Models (LLMs) are deep-learning models that achieve state-of-the-art performance in many NLP tasks. They typically consist of billions of weights. As a result, expressing weights in float32 leads to models of size at least 1GB. Such large models cannot be easily …
DHDalton HarmsenMore info
Jakub Tomczak
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Large Language Models with Knowledge GraphsDec 2024
Project description:Large Language Models (LLMs) are well-known for knowledge acquisition from large-scale corpus and for achieving SOTA performance on many NLP tasks. However, they can suffer from various issues, such as hallucinations, false references, made-up facts. On the other hand, Knowledge Graphs (KGs) can …
JLJasper LindersMore info
Jakub Tomczak
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Sustainable Diffusion ModelsAug 2024
Project description:Diffusion Models are deep-learning models that achieve state-of-the-art performance in many image synthesis tasks. They are typically parameterized with UNets and consist of billions of weights. Expressing their weights in float32 leads to models that cannot be easily deployed on edge devices (e.g., …
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Jakub Tomczak
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Deep Generative Models for (Conditional) Molecule GenerationAug 2024
Project description:Generative AI has become one of the leading approaches to (conditional) molecule generation. Like Large Language Models can learn (to some degree) rules governing natural language, could Large Chemistry Models learn rules governing atoms (quantum chemistry)? This is the leading research question of …
SDSidney DamenMore info
Jakub Tomczak
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Generative Object Detection Models for Mobile Robotics ApplicationsAug 2024
Project description:In the dynamic landscape of mobile robotics, object detection remains a foundational challenge, critical for enabling machines to interact intelligently with their surroundings. At Avular, a pioneering mobile robotics company in Eindhoven, we are excited to explore novel and innovative approaches in this …
DGDik van Genuchten
Jakub Tomczak
More infoBKBart Keulen -
Prompt Engineering for Large Language Models (taken)Dec 2023
GeneralAn internship at Accenture about prompt engineering for LLMs.RequirementsFrom our students we expect the following: high independence (including proposing own ideas);good understanding of mathematics (algebra, calculus, statistics, probability theory);good programming skills (Python + ML/DL libraries, preferably PyTorch). Thesis templatePlease take a look at this …
JBJoëlle BlinkMore info
Jakub Tomczak
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Object measurement by using computer vision (at a company)Aug 2023
Company: Datacation / aerovision.aiLocation: Eindhoven (AI Innovation Center at High Tech Campus) or Amsterdam (VU)Project descriptionAerovision.ai is a start-up that is building a no-code A.I. platform for drone companies. With this A.I. platform, companies can train, deploy and evaluate their customized computer vision algorithms, …
Jakub Tomczak
More infoACAt the company